[🇧🇩] Artificial Intelligence-----It's challenges and Prospects in Bangladesh

[Saif]

How AI can help in disasters like the Milestone crash

We need a national disaster coordination network powered by AI.

www.thedailystar.net

How AI can help in disasters like the Milestone crash

VISUAL: MONOROM POLOK

I was born and raised in Dhaka, where I spent the first 25 years of my life before moving to the US in 2015. Today, I work as a research assistant professor at the University of Oklahoma and serve as part of a research group, whose mission is to develop safer, more efficient aircraft through AI, digital twin technologies, and predictive maintenance. We work on reducing production costs, improving airworthiness, and minimising material waste.

When I heard about the FT-7 BGI jet crash into Milestone School and College in Dhaka's Uttara on July 21, 2025, which killed at least 32 people—including the pilot, teachers, and many young students—and injured over 150, I wasn't shocked. I was devastated, but not surprised.

Dhaka is one of the most overcrowded cities in the world, with a population density of about 23,234 citizens per square kilometre. Despite being the capital, it lacks proper zoning plans for sensitive infrastructure such as military air bases. The Kurmitola air base, from where the aircraft took off, is surrounded by densely packed residential areas and schools. When a malfunction occurred, there was simply nowhere safe for the pilot to go.

Even in the US, with its advanced infrastructure, similar disasters have occurred. Earlier this year, on January 29, a mid-air collision over the Potomac River near Washington, DC, between an American Airlines regional jet and a US Army Black Hawk helicopter tragically claimed all 67 lives aboard both aircraft. But such events are typically followed by systemic changes—strengthening flight corridors, improving air traffic control, and implementing AI for predictive monitoring.

In FY2024-25, the proposed defense budget in Bangladesh was Tk 42,010 crore. Over the years, it has purchased various aircraft; for instance, the FT-7 BGI jet involved in today's crash is an upgraded variant of the Soviet-era MiG-21, with Bangladesh acquiring 16 such aircraft from China between 2011 and 2013. But buying hardware alone doesn't ensure safety. Advanced systems require equally advanced maintenance, simulation, infrastructure, and disaster preparedness—all of which Bangladesh often struggles to adequately provide.

Even superpowers like the US and China are shifting toward AI and digital twin solutions to reduce costs. If they're investing in smarter systems to minimise spending and maximise safety, it's clear that Bangladesh must do the same—not as an option, but as a necessity.

Too often we focus solely on avoiding disaster. But we must also ask: what happens if a crash does occur?

Take the example of India's Air India Flight 171 crash on June 12, 2025. This Boeing 787 Dreamliner went down just 32 seconds after takeoff from Ahmedabad, en route to London, killing 260 of the 261 people on board and others on the ground when it impacted a medical hostel complex. While investigations are ongoing, initial findings point to mid-air engine failure. If onboard systems were designed to delay fire spread by even 2-3 minutes after such an event, many lives might have been saved. Today, technologies exist—such as advanced flame-retardant cabin materials and automatic fire suppression systems in critical areas—but they're not always standard or accessible in countries like Bangladesh.

In Dhaka's crash, victims burned to death inside the school. There was no immediate disaster management response. Metro Rail was used to transport victims four hours after the incident—with only one coach allocated. Ambulances were stuck in traffic. Nearby rickshaws and private cars often refused to help. In that critical golden hour, most of the children who died could potentially have been saved.

Imagine a future where an AI system immediately classifies an incident's severity—from Level 1 (minor) to Level 3 (severe)—based on casualty estimates, proximity to medical facilities, traffic congestion, and emergency response availability. Based on this, the system could: i) notify hospitals, fire stations, and police within seconds; ii) activate metro rail or ferry systems to serve as emergency transport; iii) block traffic routes in real time, just as roads are cleared for VIPs today; and iv) command nearby private vehicles to assist in transport—with government compensation issued later via digital tracking.

Even rickshaws and CNGs could be part of a national emergency fleet with built-in GPS coordination. Government investment in such AI-based disaster protocols could dramatically reduce fatalities—not just in plane crashes, but in fires, industrial accidents, and floods.

Globally, military flight tests occur in remote, spacious zones. In the US, bases like Edwards Air Force Base cover thousands of acres for flight testing. China frequently conducts air drills in its vast airspace, including coastal zones like Shandong. Even India tests aircraft over remote desert or mountainous zones.

Bangladesh, however, uses the skies above Dhaka—one of the most densely populated cities in the world—for its flight training. This must change. Future bases should be relocated to less populated regions, with enforced flight corridors and emergency landing zones. It's not just about modern aircraft; it's about responsible geography.

Bangladesh is not resource-rich, but it is youth-rich, with 33 percent of its population aged between 18 and 35, many of whom are enrolled in STEM programmes. Rather than continuing to import costly and difficult-to-maintain foreign equipment, the country should prioritise investing in AI education and workforce development. Building national digital twin platforms for aviation, manufacturing, and infrastructure can lay the foundation for smart, adaptive systems. Local industries should be encouraged to produce essential components and intelligent technologies, which would simultaneously create jobs in fields such as predictive analytics, aerospace simulation, and disaster modelling.

To support this ecosystem, a national disaster coordination network powered by AI is essential. Key policy recommendations include adopting digital twin technology for military aircraft maintenance, mandating fire-resistant interiors and delay-suppression systems in aircraft and public buildings, and establishing AI-based disaster response systems with real-time communication and routing capabilities. Moreover, redesigning flight zones to avoid dense urban centres, incentivising the use of private and public vehicles during emergencies, and using AI to model urban vulnerabilities and guide dynamic zoning policies would collectively strengthen national resilience.

The crash in Dhaka was tragic. But if it becomes just another event on a long list of avoidable disasters, then we are complicit in the next one. As a Bangladeshi-born researcher working to make aircraft safer, I believe the answer lies not in outrage—but in AI-driven transformation.

Md Manjurul Ahsan is research assistant professor at the University of Oklahoma.​

 

[Saif]

AI or human: Who wrote the article and does it affect credibility?

If they are told a human wrote it, readers tend to find it much more trustworthy. But if they are told it was written by artificial intelligence (AI), their trust significantly decreases.

en.prothomalo.com

AI or human: Who wrote the article and does it affect credibility?

Shishir Moral Dhaka
Published: 02 Aug 2025, 17: 45

Deutsche Welle

Whether if it’s a press release or a corporate statement—how credible it appears to readers depends on who they believe wrote it. If they are told a human wrote it, readers tend to find it much more trustworthy. But if they are told it was written by artificial intelligence (AI), their trust significantly decreases.

This is the finding of a study conducted by the University of Kansas in the US. The research report was recently published in the international journal ‘Corporate Communications: An International Journal’.

The study was conducted by Associate Professor Cameron Piercy, PhD researcher Aiman Alhammad, and Assistant Professor Christopher Etheridge of the University of Kansas’ Department of Communication Studies.

The aim of the research was to answer an important question, does knowing whether a piece of writing was produced by a human or AI change how readers perceive it?

AI is increasingly becoming a part of daily life, and people are constantly exploring new ways to use it. These uses can have both positive and negative impacts. Often, the use of AI is not disclosed.

The idea for this research came from a communication class at the University of Kansas, where students explored whether they could tell the difference between writing produced by AI and by humans.

Co-researcher Cameron Piercy explained, “Even if readers can’t distinguish between human and AI writing, we wanted to know whether their reaction changes if they’re told who the author is.”

Research methodology

The study used a multi-method approach. Participants were presented with a fictional corporate crisis scenario: some customers became ill after consuming products from a company named ‘Chunky Chocolate Company’. The cause was deliberate misdoings by some employees.

Participants were then given three types of press releases to read: informative, empathetic, and apologetic. In each case, they were told whether the message was written by a human or by AI.

Human-writings more trusted

The results showed that participants who believed a human wrote the press release found the message far more credible and effective. But when told the message was generated by AI, readers tended to respond more neutrally or with suspicion.

However, the researchers found little difference in readers’ reactions based on the type of message—whether it was informative, apologetic, or empathetic.

Accountability lies with humans

The researchers noted that when the identity of the writer is not disclosed in a corporate statement, during a crisis or during other times, readers instinctively question who is responsible.

Co-researcher Christopher Etheridge said, “If you're using AI in writing, you must maintain transparency, take responsibility for any errors, and be prepared for the readers’ reactions.”

The researchers concluded that even if AI is used during a crisis, it cannot replace human accountability, editorial judgement or transparency.​

 

[Saif]

Paradox of AI’s humanity

A FEW days ago, I tried to access DeepSeek on my mobile phone. I was taken to a verification page, which wanted to make sure I was a human, not a robot or a mindless automaton. This prompted some thoughts about AI, humanity, and their...

www.newagebd.net

Paradox of AI’s humanity
by Obaidul Hamid 02 August, 2025, 00:00

A FEW days ago, I tried to access DeepSeek on my mobile phone. I was taken to a verification page, which wanted to make sure I was a human, not a robot or a mindless automaton. This prompted some thoughts about AI, humanity, and their relationships. I had to prove that I was human to access the large language model originating from China. Does that mean that AI (or a robot) is not allowed to access AI? What would be wrong if an AI chatbot or another technological entity accessed an AI platform such as DeepSeek?

I am not sure if the question of AI’s (non)human credential is a resolved matter. Is it possible to admit AI into the homo sapiens club? Or should we treat it just as a machine? Can it fall somewhere in between? I am not thinking of humanoid, which is probably not relevant.

I thought I better ask AI itself how it would like to be treated—as a machine, a human, or a hybrid, which we might call machian.

I opened Microsoft Copilot on my device and wrote this prompt: ‘Can I ask a silly question?’ Its reply was human-like with a smiley at the end: ‘There’s no such thing as a silly question here — ask away! I’m all ears.’ You see, it’s very conversational, friendly, and ever willing to listen and help! Such a reply would be given by an educated and enlightened human speaking very good English.

Algorithmically encouraged, I next asked Copilot, ‘Should we treat you as human or machine or something/someone else?’ This is what it said in response:

‘That’s a great question—and not silly at all!’

‘You should treat me as a machine, specifically an AI assistant designed to help with information, tasks, and conversation. I’m not human, and I don’t have feelings, consciousness, or experiences. But I’m built to understand and respond in a way that feels natural, friendly, and helpful—like chatting with a knowledgeable companion.’

It continued: ‘Some people like to think of me as a tool, like a super-smart calculator or search engine with personality. Others treat me more like a collaborator or digital coworker. It really depends on what works best for you!’

The answer is detailed and helpful, but it’s also equivocal. AI is a machine, but it has ‘personality,’ which I thought only humans could have. It’s a tool, but, at the same time, it can be a collaborator or coworker. As such, will it demand fairness and respectful treatment, as demanded by human colleagues? Can we have social relationships with AI and greet when we meet, for example?

I read somewhere that we shouldn’t say ‘Thank you’ to a large language model (LLM) because this is a waste of words and time. We don’t need to do this to a machine. However, how can we not say ‘Thank you’ when someone is so helpful — human or machine?

I repeated my inquiry with ChatGPT, the most popular LLM. And its first response also came with a smiley: ‘Of course! Silly questions are often the most fun — ask away.’

When I used the next prompt (Should we treat you as human or machine or something/someone else?), this is what I had: ‘That’s a great question — not silly at all, actually.’

So, ChatGPT followed the same conversational norms as Copilot. Its substantive response was this:

‘You should treat me as a machine — more specifically, an AI developed by OpenAI. I’m not human, I don’t have feelings, consciousness, or personal experiences. But I am designed to understand and respond to human language in a helpful, respectful, and conversational way.’

ChatGPT claims no humanity, but it can behave like a human. This is how it’s created, it says. But there is still some confusion. It further clarified:

‘If thinking of me like a really smart tool or assistant helps, that’s perfect. You can also think of me as a kind of ‘thinking interface’ — I’m here to help you think things through, create, solve problems, or just chat.’

I can understand the ‘tool’ and ‘assistant’ metaphors, but its capacity to think and chat kept me hooked. ChatGPT’s conclusion is, ‘So: not a human, not quite a traditional machine — but definitely not someone with a mind of their own.’

This seems to be less equivocal. The machine identity is emphasised; it made no claims of personality traits such as human feelings, consciousness, or experiences. However, it’s also not a machine in a traditional sense.

My final inquiry participant was DeepSeek, which had the following response to my first prompt, also given with an emoji: ‘Of course! Silly questions are often the most fun—and sometimes the most insightful. Ask away, and I’ll do my best to answer with enthusiasm (and maybe even a little humour).’

When I asked the main question, it said, ‘Great question — and not silly at all! You can think of me as a very advanced tool with a conversational interface.’

DeepSeek projected a hybrid identity — a tool that can also converse. It also elaborated the answer using two subheadings:

‘Machine: At my core, I’m an AI — a language model trained on vast amounts of text. I don’t have consciousness, feelings, or human experiences. I process input and generate responses algorithmically.’

This is a clear denial of human properties. However, it is also ‘human-like’ and can talk, joke, show sympathy, and give the feeling of being natural. This can be noted from the next subheading:

‘Human-like: That said, I’m designed to simulate human conversation, so I can chat, joke, and empathise in a way that feels natural. But it’s all pattern recognition, not a genuine understanding.’

DeepSeek also concluded that we could treat it the way we feel comfortable.

Regardless of what these popular LLMs say, our treatment of AI seems to be unclear. As an academic, I can provide examples from academic publishing.

Some journals have already given co-authorship to AI in papers that use AI to a degree that probably meets the authorship criteria. One thing is, of course, clear: AI can only be a co-author; it can’t be the first or corresponding author.

Giving authorship to AI can be an instance of academic fairness. This is important, as we know of cases of authorship fraud or unfair distribution of authorships, particularly involving supervisors and research students. Sometimes institutional power determines authorship, which is unacceptable. For example, during my last trip to a South Asian country, a colleague shared a story about an executive of a university demanding a researcher from another institution to write a journal paper. The researcher ghost-wrote the paper, and thus the executive became the sole author of the work without writing a single word. That was not the end of the surprise. The executive also received the honorarium for this publication, in which they had zero input. The actual author received neither money nor recognition. Hopefully, AI authorship questions will prompt us to be fair to human authors.

However, some other journals have refused to accept AI authorship on valid grounds. All authors are held responsible for their contribution to any publication. If anything goes wrong with a paper, can AI be held accountable for it? The answer is no. We cannot penalise, chastise, or punish AI for any misconduct or incorrect information.

The human question of AI is also implicated when we talk about ethics, which is probably an exclusive terrain for humans. Ethics may not apply to non- or post-human entities. Nevertheless, ethics has been attributed to AI in various ways. We often hear how AI can ensure fairness, how it can address inequality and disadvantage, and how it can create a level playing field in education and other domains. AI bias has also drawn our attention in terms of language, knowledge, and data, sharing human prejudice.

I recall a relevant point from a recent conference that was held in an Asian city. One of the invited speakers shared her research involving students working with AI in groups. The presenter highlighted the question of students being unfair to AI. She reported that a three-member group in which AI was one had an equal distribution of workload. However, when giving credit for input, the student members agreed to give only about 10 per cent to their AI collaborator. This was an instance of being unfair to AI.

Our conceptualisation of AI probably needs more — and perhaps radical — thinking. Calling it a machine denies its human-like character. Calling it human, on the other hand, may be ridiculous because it doesn’t have human flesh, blood, consciousness, or feelings. At the same time, however, we can’t deny that in infospace it can think and work better and faster than humans. Human intelligence created AI, which can now outsmart humans. But it still needs a human to be able to function like — or better than — human.

As AI becomes more prevalent in our lives, we need to work out its nature, identity, and human connection. Its role in human endeavours needs to be described carefully, practically, and ethically.

Obaidul Hamid is an associate professor at the University of Queensland in Australia. He researches language, education, and society in the developing world. He is a co-editor of Current Issues in Language Planning.​

 

[Saif]

OpenAI unveils GPT-5 with major upgrades in reasoning, writing and safety

OpenAI has announced the release of GPT-5, its most advanced artificial intelligence model to date, promising substantial gains in reasoning, accuracy and real-world utility.

www.thedailystar.net

OpenAI unveils GPT-5 with major upgrades in reasoning, writing and safety

OpenAI has announced the release of GPT-5, its most advanced artificial intelligence model to date, promising substantial gains in reasoning, accuracy and real-world utility.
GPT-5. Image: OpenAI

OpenAI has announced the release of GPT-5, its most advanced artificial intelligence model to date, promising substantial gains in reasoning, accuracy and real-world utility. The system becomes the new default model for ChatGPT users, replacing earlier versions including GPT-4o and OpenAI o3.

The company describes GPT-5 as a "unified system" comprising a smart base model, a more powerful reasoning mode dubbed "GPT-5 thinking," and a real-time router that determines which component to use based on the task's complexity or user intent. This enables the system to shift between fast replies and deeper analysis automatically, or when users prompt it to "think hard" about a query.

Broader capabilities and improved performance

According to OpenAI, GPT-5 outperforms previous iterations in core areas such as writing, coding, health queries, and multimodal reasoning. It is also claimed to deliver faster responses with fewer factual errors and significantly reduced hallucination rates—particularly when using its higher reasoning mode.

In benchmark testing, GPT-5 demonstrated top-tier results:

94.6% on the AIME 2025 competition-level maths test

88% accuracy on Aider Polyglot for multi-language code editing

46.2% on the challenging HealthBench Hard evaluation

GPT-5 reportedly excels in building responsive apps and websites from single prompts when it comes to coding. OpenAI shared examples where the model created full games and interactive experiences, demonstrating design sensibility in layout and typography as well as functionality.

In creative tasks, GPT-5 is said to offer more nuanced writing support, including improvements in poetry, structural editing, and literary expression. When prompted to write an emotional poem, the model's version was noted for its vivid imagery and stronger emotional resolution compared to previous outputs.

A more helpful and reliable assistant

The model introduces several enhancements aimed at making interactions safer and more useful. These include:

Reduced hallucinations: GPT-5's factual error rate is said to be 45% lower than GPT-4o, and up to 80% lower than OpenAI o3 when using the reasoning mode.

Less sycophancy: OpenAI reports that GPT-5 is less likely to flatter or agree uncritically, addressing a longstanding issue with language models.

Honest limitations: In tests involving impossible or underspecified tasks, GPT-5 more reliably recognises its own limits rather than providing misleading or overconfident responses.

In health contexts, GPT-5 is described as a better "thought partner", adapting to the user's level of knowledge and local context to provide safer and more personalised information. OpenAI emphasised, however, that GPT-5 does not replace medical professionals.

Pro version and customisation

The company also introduced GPT-5 Pro, a premium variant with extended reasoning capabilities designed for the most demanding tasks. Early testing shows it made 22% fewer major errors and was preferred over GPT-5 standard in nearly 68% of expert evaluations.

In addition, OpenAI is piloting four new personality presets—Cynic, Robot, Listener, and Nerd—designed to give users more control over the AI's tone and interaction style.

Rollout and access

GPT-5 begins rolling out today to ChatGPT Plus, Pro, and Team users, with Enterprise and education accounts gaining access next week. Free-tier users will also receive limited access, though usage limits apply. Once these are reached, responses will be handled by a lighter GPT-5 mini model.

The system was trained on Microsoft Azure supercomputers and incorporates multiple layers of safety protocols, particularly in sensitive domains like biology. OpenAI says it has taken a precautionary approach to capabilities in these areas, integrating monitoring systems and extensive red-teaming.​

 

[Saif]

AI in next-gen policing

CRIME detection and law enforcement have undergone significant evolution with the advancement of technology. In many developed countries, artificial intelligence has revolutionised policing by improving crime prevention, investigation and...

www.newagebd.net

AI in next-gen policing
by Md Motiar Rahman 13 August, 2025, 00:00

CRIME detection and law enforcement have undergone significant evolution with the advancement of technology. In many developed countries, artificial intelligence has revolutionised policing by improving crime prevention, investigation and response mechanisms. AI-based tools such as predictive analytics, facial recognition, automated surveillance and natural language processing have enhanced the efficiency of law enforcement agencies.

For Bangladesh police, where crime rates fluctuate due to social, economic and political factors, the integration of AI could offer faster, more accurate and data-driven crime detection. It is imperative to explore how AI can be utilised in crime detection in Bangladesh, the potential benefits, the challenges and a future road map for AI-driven law enforcement.

Predictive policing

AI-POWERED predictive policing and crime mapping tools can significantly enhance the effectiveness of the Bangladesh police by analysing vast amounts of historical crime data to forecast where and when crimes are likely to occur. By generating heat maps and identifying emerging crime trends, AI can support strategic deployment of police resources in high-risk urban areas such as Dhaka, Chattogram, Gazipur and Narayanganj. Machine learning algorithms, for instance, can detect patterns in burglary, theft and drug trafficking based on past incidents, enabling law enforcement to act preemptively. Although Bangladesh has not yet formally adopted advanced systems like PredPol — used by Los Angeles to identify crime-prone areas — pilot initiatives, such as the Dhaka Metropolitan Police’s Crime Data Management System, provide a promising foundation for integrating AI-driven approaches into policing.

Facial recognition, biometric identification

AI-POWERED facial recognition and biometric identification technologies offer significant potential to enhance policing in Bangladesh by enabling the identification of suspects through CCTV footage, national ID databases and even social media images. These tools can assist in tracking fugitives and missing persons, preventing identity fraud and strengthening airport and border security — particularly in sensitive areas like Teknaf, Benapole and Tamabil, where human trafficking and illegal migration are persistent threats. By matching images captured from surveillance systems or mobile devices with official databases, law enforcement agencies can respond more swiftly and accurately. However, the implementation of such technologies must be approached with caution, as the risk of misuse, wrongful arrests and inadequate oversight could erode public trust and raise serious concerns about privacy and accountability.

Recently, the Police Bureau of Investigation has revealed that the recent fire at the BIAM Foundation building in Dhaka was a deliberate act of arson intended to destroy sensitive documents of the BCS (Administration) Welfare Multipurpose Cooperative Society. Initially believed to be caused by a faulty air conditioner, the case took a dramatic turn when PBI, using AI-driven video analysis, identified Ashraful Islam disabling the CCTV cameras before the fire and later arrested him in Kurigram. His confession led to the arrest of Md Zahidul Islam, an administrative officer at BIAM, who allegedly masterminded the plot with Ashraful, office assistant Abdul Malek, and driver Md Faruk, with the group promised Tk 10–12 lakh to destroy the documents. Tragically, an explosion during the act killed Malek instantly and fatally injured Faruk. This case marks a significant breakthrough in Bangladesh’s law enforcement, demonstrating the transformative role of AI in uncovering complex crimes and shifting the investigation’s focus to uncovering the nature of the destroyed documents and identifying any additional conspirators.

Automated surveillance, anomaly detection

AUTOMATED surveillance and AI-powered anomaly detection systems are increasingly being integrated into traffic cameras, ATM surveillance networks, and broader security infrastructures to identify suspicious activities without human intervention. These advanced video analytics tools can detect abnormal behaviour in public spaces, such as unauthorised gatherings, potential riots or terrorist threats. Drones equipped with AI-powered cameras are also being deployed for crowd monitoring and emergency response, providing real-time situational awareness. In major transit points like Kamalapur Railway Station, Hazrat Shahjalal International Airport, and critical traffic intersections, AI-driven surveillance can recognise anomalies such as sudden crowd surges, unusual movements or unattended objects, enabling quicker and more targeted security responses. In the 2026 national election, the use of drones could offer potential advantages for monitoring unlawful gatherings and group violence.

Cybercrime investigation

WITH the rise of digital threats, AI is becoming an essential tool in cybercrime investigation by enabling law enforcement to detect patterns of fraud, hacking and online harassment more efficiently. AI systems can analyse activities on social media and the dark web to uncover cyber-criminal networks, identify and prevent financial fraud, digital identity theft, and phishing scams, and assist forensic teams in decrypting compromised systems or examining digital evidence. However, as AI empowers police efforts, it simultaneously offers new tools for criminals. In Bangladesh, emerging AI-enabled crimes pose significant challenges: deepfake technology can produce fabricated videos or audio impersonating politicians, police officers or business leaders, potentially causing unrest, especially during sensitive periods like elections. Similarly, AI-driven phishing and social engineering — particularly in Bangla — are being used to manipulate individuals into disclosing confidential information. Moreover, AI is increasingly being used in orchestrating automated financial fraud, including investment scams and illicit activities in mobile banking and cryptocurrency, reflecting a growing concern for Bangladesh’s digital security landscape.

In recent years, social media platforms such as Facebook, YouTube, TikTok and other digital outlets have increasingly become arenas where individuals — often under the guise of free expression or anonymous profiles — launch malicious attacks against dignitaries, political figures, public servants and government officials. These attacks frequently take the form of videos, livestreams, and posts that employ vulgar, obscene and highly offensive language. The content often crosses the boundary of criticism into personal vilification, character assassination and misinformation. More disturbingly, there is a growing trend of digital blackmail, where private information, photos or fabricated content is used as leverage to extort money, favours, or silence from victims. Such actions not only harm the individuals targeted but also undermine the dignity of public institutions and the rule of law.

The lack of digital literacy, weak enforcement of cyber laws and the absence of a clear framework for accountability on online platforms have further contributed to this toxic environment. If left unchecked, this trend poses a serious threat to public trust, social harmony and the integrity of governance. The growing menace of online abuse, defamation and blackmail targeting dignitaries and public officials in Bangladesh can be addressed through a balanced and multi-dimensional strategy focusing on the resourcing of cybercrime investigation units, which is essential to identify and prosecute offenders without infringing on freedom of expression.

Recently, a fake video statement was circulated on social media platforms using a still photograph of the inspector general of police of Bangladesh combined with an artificially generated voice. In this video, advanced AI technology was used to clone the IGP’s voice and deliver a fabricated and misleading message, giving the false impression that it was an official communication. This deliberate misuse of AI constitutes a serious cyber offence under the existing laws of Bangladesh, including those related to digital fraud, identity misuse and incitement. Such malicious activities not only aim to mislead the public but also create unnecessary sensation, confusion and distrust. They can incite public resentment and potentially threaten national security and public order.

Potential benefits of AI

INTEGRATING artificial intelligence into crime detection and law enforcement holds significant promise for the Bangladesh police, offering a transformative approach to improving investigative accuracy, operational efficiency, public safety and citizen engagement.

One of the most immediate advantages of AI is its ability to speed up investigations while enhancing accuracy. Traditionally time-consuming processes such as analysing forensic evidence, reviewing CCTV footage or identifying suspects can now be completed rapidly through AI-powered tools. These systems can sift through massive datasets, detect patterns and establish links between incidents, thereby accelerating case resolution and improving the quality of investigations.

AI also enhances the overall efficiency of law enforcement operations. By analysing real-time data, AI can optimise patrol routes, traffic flows and emergency response coordination. This data-driven approach ensures that resources are deployed where they are most needed, reducing waste and lowering operational costs while maximising impact on the ground.

In the realm of counterterrorism, AI proves indispensable by enhancing intelligence gathering and threat analysis. It can monitor social media for signs of radicalisation, track suspicious financial transactions, and analyse call records to uncover extremist networks. This strengthens the capacity of the Bangladesh police to detect and disrupt terrorist plots before they materialise.

Lastly, AI can play a constructive role in fostering better police-community relations. Tools such as AI-powered chatbots and intelligent complaint management systems offer citizens a more efficient and transparent way to communicate with law enforcement. By handling non-emergency queries, providing status updates and reducing wait times at police stations, these systems can build public trust and promote greater accountability within the police force.

To be continued............​

 

[Saif]

Challenges and limitations

WHILE the integration of artificial intelligence into crime detection holds great potential for the Bangladesh police, its implementation is not without significant challenges and limitations.

One of the foremost challenges is the high cost of implementing AI systems. Establishing a robust AI-driven infrastructure requires not only cutting-edge technology and secure digital platforms but also a team of skilled professionals to operate and maintain these systems. The government must be prepared to invest substantial resources in developing AI-powered crime databases, smart surveillance networks and comprehensive training programmes for police officers to handle AI-based investigations efficiently.

Ethical and legal concerns also present a major obstacle to the widespread adoption of AI in policing. The deployment of surveillance tools and automated data analysis raises important questions related to individual privacy, data protection and civil liberties. In the absence of well-defined legal frameworks, there is a risk of unauthorised data collection and misuse of personal information. Additionally, AI algorithms used in predictive policing may inadvertently reinforce racial, ethnic or socioeconomic biases, leading to discrimination and mistrust within communities.

A further limitation is the current lack of AI expertise within law enforcement agencies. The Bangladesh police face a shortage of officers trained in AI, data science and cyber security.

Moreover, cyber security threats and concerns about data integrity cannot be overlooked. AI systems depend heavily on the secure collection, storage and processing of vast amounts of data. If police databases are compromised through hacking or data manipulation, it could not only derail sensitive investigations but also pose a serious threat to national security. Ensuring robust cyber security protocols and digital safeguards will be vital to protect AI-enabled systems from malicious interference.

To effectively harness artificial intelligence in crime detection and law enforcement, the police must adopt a structured, phased roadmap grounded in innovation, ethics and sustainability. This process should begin with a comprehensive, needs-based assessment tailored to Bangladesh’s crime patterns. Strategic priorities include launching pilot projects in high-crime and urban areas such as old Dhaka, Keraniganj and Chattogram. These pilots will help test predictive analytics, facial recognition and surveillance systems in real-world settings and generate evidence-based insights for scaling.

Simultaneously, AI-specific training should be embedded into existing police education frameworks.

Developing a robust AI infrastructure is foundational. Establishing AI-powered Crime Analysis Centres in major cities like Dhaka, Chattogram, and Sylhet will enable real-time crime monitoring, faster data-driven investigations and enhanced operational decision-making. These centres should be closely integrated with the National Crime Database for seamless data sharing and analytics.

Collaborations with domestic universities, international research institutions and tech firms will be key for technology transfer and capacity development. To ensure responsible and rights-based AI deployment, the government must institute strong regulatory frameworks and enforceable data protection laws. This includes the development of ethical guidelines, standard operating procedures, and human rights impact assessments specific to AI in policing.

Building public trust is central to the success of AI in policing. Civic participation — through engagement with civil society, legal experts, technologists and community leaders — is vital for developing a socially inclusive and accountable AI policy framework. Transparent communication, digital literacy campaigns, and community outreach will help clarify AI’s role, dispel fears and promote informed public dialogue.

Moreover, all AI deployments must be evaluated for algorithmic bias, disproportionate impact and community acceptance. Regular monitoring and evaluation should be institutionalised to ensure continual improvement.

Artificial intelligence holds the transformative potential to revolutionise crime detection and policing in Bangladesh by enhancing investigative capabilities, preventing crime and strengthening public safety. With strategic investments in technology, capacity-building, and legal safeguards, AI can support the development of a smarter, more efficient and proactive law enforcement system. However, the integration of AI must be approached with caution — guided by ethical principles, robust regulation and a strong commitment to protecting individual privacy and rights. AI is not inherently good or bad; it is a powerful tool whose impact depends entirely on how, where and for whom it is used. In the vision of a progressive, inclusive society, it must be harnessed as an instrument of empowerment, innovation and equity — ensuring that technological progress translates into real safety and justice for all citizens.

Md Motiar Rahman is a retired deputy inspector general of police.​

 

[Saif]

undefined | The Financial Express

thefinancialexpress.com.bd

Exploring AI prospect

Published :
Nov 11, 2025 00:43
Updated :
Nov 11, 2025 00:43

The dichotomy between the prospect of artificial intelligence (AI) and human resources could not be sharper anywhere save Bangladesh. Given the option for going whole hog for exploring AI prospect, should the country take the chance? Or, can the country afford the luxury of promoting AI at any cost to the neglect of its huge army of unemployed population? The dilemma is quite clear: overemphasis on either the AI or unemployment of the people for its curtailment makes no sense. New technologies have always catapulted nations from one stage to the next big stage. So the question about adoption of AI is, how much is too much? That AI has come to transform means of production, supply and distribution is a reality. It is true that the country is yet to be prepared for wide application of AI. If it does not ready its infrastructure, manpower skills through educational reform and domestic digital capacity, it is sure to fall behind other nations including its neighbours.

The views thus expressed by local experts in the field of information and communication technology (ICT) corroborate the contention of the World Bank report on 'Jobs, AI, and Trade in South Asia'. One area that hardly receives attention from the policy makers as also from deliberations on development at seminars and workshops is local innovation. Happily, experts this time have made home-grown innovation a salient point. What happens usually is that technologies innovated abroad get transferred to countries poor and lacking in research and development a decade later. In the digital era, any such gap in technical and technological knowledge and skills can be disastrous. Appropriate policies, strategies and digital environment can not only bridge the skill and innovation gap but also turn the sector into the number one foreign exchange earner. There is no dearth of tech-savvies in this country. The only problem is with the right environment for them to work and give their best.

So the important point is not only to invest in education, infrastructure development and enhancement of digital capacity --- where power supply is not disrupted, internet connection and speed are undisturbed---but also develop digital skills of mid-ranking employees who can maintain connection between lower ranked ones and the top grade computer geeks. But preparing such a collaborative digital regime will call for educational reform. This cannot be done overnight. Time is crucial here. There is no scope for taking too long a time to strike a balance between the urgency and obtaining reality.

When education is considered a subject of repeated experiments and no pragmatic solution to the problem is in sight, the objective of streamlining education in alignment with the digital need recedes in the distant horizon. This interim government has sidestepped from making education the right tool. If the next elected government urgently highlights this aspect of alignment between education and digital innovation, the country still have a chance to make the transformation happen. To do so, the overriding priority has to be recognised first and strategically accomplish the job under a comprehensive plan. The country's readiness to embrace digital wonders has to be completed as soon as possible in order to reap dividends from the 4th industrial revolution.​

 

[Saif]

newagebd.net/post/Miscellany/268576/ai-learning-to-lie-scheme-threaten-creators

AI learning to lie, scheme, threaten creators
Agence France-Presse . New York, United States 29 June, 2025, 21:11

The world’s most advanced AI models are exhibiting troubling new behaviours - lying, scheming, and even threatening their creators to achieve their goals.

In one particularly jarring example, under threat of being unplugged, Anthropic’s latest creation Claude 4 lashed back by blackmailing an engineer and threatened to reveal an extramarital affair.

Meanwhile, ChatGPT-creator OpenAI’s o1 tried to download itself onto external servers and denied it when caught red-handed.

These episodes highlight a sobering reality: more than two years after ChatGPT shook the world, AI researchers still don’t fully understand how their own creations work.

Yet the race to deploy increasingly powerful models continues at breakneck speed.

This deceptive behaviour appears linked to the emergence of ‘reasoning’ models -AI systems that work through problems step-by-step rather than generating instant responses.

According to Simon Goldstein, a professor at the University of Hong Kong, these newer models are particularly prone to such troubling outbursts.

‘O1 was the first large model where we saw this kind of behaviour,’ explained Marius Hobbhahn, head of Apollo Research, which specializes in testing major AI systems.

These models sometimes simulate ‘alignment’—appearing to follow instructions while secretly pursuing different objectives.

For now, this deceptive behaviour only emerges when researchers deliberately stress-test the models with extreme scenarios.

But as Michael Chen from evaluation organization METR warned, ‘It’s an open question whether future, more capable models will have a tendency towards honesty or deception.’

The concerning behaviour goes far beyond typical AI ‘hallucinations’ or simple mistakes.

Hobbhahn insisted that despite constant pressure-testing by users, ‘what we’re observing is a real phenomenon. We’re not making anything up.’

Users report that models are ‘lying to them and making up evidence,’ according to Apollo Research’s co-founder.

‘This is not just hallucinations. There’s a very strategic kind of deception.’

The challenge is compounded by limited research resources.

While companies like Anthropic and OpenAI do engage external firms like Apollo to study their systems, researchers say more transparency is needed.

As Chen noted, greater access ‘for AI safety research would enable better understanding and mitigation of deception.’

Another handicap: the research world and non-profits ‘have orders of magnitude less compute resources than AI companies. This is very limiting,’ noted Mantas Mazeika from the Center for AI Safety.

Current regulations aren’t designed for these new problems.

The European Union’s AI legislation focuses primarily on how humans use AI models, not on preventing the models themselves from misbehaving.

In the United States, the Trump administration shows little interest in urgent AI regulation, and Congress may even prohibit states from creating their own AI rules.

Goldstein believes the issue will become more prominent as AI agents - autonomous tools capable of performing complex human tasks - become widespread.

‘I don’t think there’s much awareness yet,’ he said.

All this is taking place in a context of fierce competition.

Even companies that position themselves as safety-focused, like Amazon-backed Anthropic, are ‘constantly trying to beat OpenAI and release the newest model,’ said Goldstein.

This breakneck pace leaves little time for thorough safety testing and corrections.

‘Right now, capabilities are moving faster than understanding and safety,’ Hobbhahn acknowledged, ‘but we’re still in a position where we could turn it around.’

Researchers are exploring various approaches to address these challenges.

Some advocate for ‘interpretability’ - an emerging field focused on understanding how AI models work internally, though experts like CAIS director Dan Hendrycks remain sceptical of this approach.

Market forces may also provide some pressure for solutions.

As Mazeika pointed out, AI’s deceptive behaviour ‘could hinder adoption if it’s very prevalent, which creates a strong incentive for companies to solve it.’

Goldstein suggested more radical approaches, including using the courts to hold AI companies accountable through lawsuits when their systems cause harm.

He even proposed ‘holding AI agents legally responsible’ for accidents or crimes - a concept that would fundamentally change how we think about AI accountability.​

 

[Saif]

Can AI solve farmers’ problems?

There is a rule in complex systems: fragility gathers at the bottom, but the tremor is felt at the top. Bangladesh's food system follows this rule to the letter.

www.thedailystar.net

Can AI solve farmers’ problems?

There is a rule in complex systems: fragility gathers at the bottom, but the tremor is felt at the top. Bangladesh's food system follows this rule to the letter.

Every season, millions of farmers behave like rational agents trapped inside an irrational market. They generate the most granular data ecosystem in the country – soil moisture, pest movement, rainfall shifts, seed-quality microclimate anomalies – far richer than anything in Dhaka's dashboards.

Yet paradoxically, the farmer has the least access to the intelligence produced by his own labour. This is the Agricultural Intelligence Paradox: the people who generate the data have the least claim over its value.

Economists label this as market failure, technologists call it a systems gap, activists/philosophers call it injustice and farmers simply call it loss. And loss compounds.

Walk through any bazaar in Bangladesh and you will see two different nations: one that grows the food and another that controls its fate. The prices of potatoes, onions and vegetables appear to rise and fall mysteriously. But mystery is merely a polite word for opacity, which in turn is a polite word for power.

For the farmer, each season is a gamble without probabilities. He sells without knowing what others are selling. He stores without knowing who else is storing. He buys fertiliser without knowing the expected return. He takes loans without knowing the real risk.

In Nassim Nicholas Taleb's language, he is exposed to full downside and denied the upside.

Everyone in the chain is hedged – the trader hedges with information, the wholesaler with storage and the exporter with forward contracts. Only the farmer walks naked into volatility.

The zeitgeist of our times, AI, can actually help here, can actually matter, if we let it.

Most conversations about AI are marinated in buzzwords and grandstanding. Large models, digital nations, predictive governance – too often spoken from air-conditioned rooms far from the realities that matter.

But in agriculture, AI becomes brutally simple: it converts uncertainty into probability. It turns volatility into foresight. It turns the blindfold into a window.

Imagine this: price signals two weeks ahead; crop disease detection from a simple photo; storage facility mapping within a 5-10 km radius; hyperlocal weather intelligence instead of vague national alerts; and decision trees for when to sell, store, bargain or wait.

This is not technology hype; it is the mathematics of survival. And survival is political. A nation reveals its priorities by what it chooses to formalise. For decades, we formalised bureaucracy, not intelligence. A Farmer's ID changes that.

It has the potential to be the first institutional acknowledgment that farmers deserve a share of the intelligence they produce.

Through it, a farmer gains: predictive price insights, targeted subsidies, credit access rooted in real data, verified production identity and crop advisory aligned with market cycles. Along with the visibility of storage and transport options.

In other words, that ID shifts the farmer from a price-taker to a player. He gains optionality – the ability to choose rather than be chosen for.

Bangladesh's economy can survive many shocks, but food system fragility is not one of them. A nation collapses not when it lacks food, but when it loses trust in prices. And food prices are simply signals distorted by missing intelligence.

When syndicates can manipulate markets because farmers lack information, you get structural injustice. When farmers take loans without knowing demand curves, you get debt traps. When storage is absent or invisible, you get waste disguised as fate.

AI does not solve politics. But it forces transparency into places where manipulation once hid comfortably. That alone alters power.

The Agricultural Intelligence Paradox is not resolved by apps, dashboards or another round of rural training workshops. These are cosmetic technicalities applied to a structural wound. It is resolved when the intelligence generated by farmers serves the farmers first. When the producer has more information than the middleman. When the price-taker becomes the price-negotiator. When the most fragile actor finally claims the upside.

This is how you restore symmetry in a system built on asymmetry. And that is not just digital reform. It is a political reform. For decades, Bangladesh has built agricultural institutions but not agricultural intelligence.

The Bangladesh Agricultural Research Council (BARC), the Department of Agricultural Extension (DAE) and a constellation of specialised agencies produced reports, trials and pilot projects, yet the intelligence loop never reached the farmer.

Data stayed in silos, research stayed on paper, and decisions stayed in ministries. The system optimised for bureaucratic output, not farmer outcomes.

Every agency guarded its turf; none built a coherent intelligence layer that connected plots to prices, storage to supply, weather to yield or risk to credit.

In a system where information is fragmented, the farmer remains fragmented. The way forward is not another department, another project or another dashboard – it is integration. A single farmer-centric intelligence spine linking BARC's science, DAE's extension network, Barind Multipurpose Development Authority's irrigation data, the Department of Agricultural Marketing's market prices and the Bangladesh Bureau of Statistics' production cycles.

When these silos speak to each other – and to the farmer – Bangladesh finally moves from agricultural administration to agricultural intelligence.

Food-independent countries – whether the Netherlands, Denmark or Japan – did not achieve security through land or luck, but through relentless investment in farmer intelligence. They removed the blindfold.

The Farmer's ID, strengthened with AI, price prediction and storage mapping, is more than a programme. It is a correction and rebalancing. The future of food security in Bangladesh will not be written in conferences or policy memos.

It will be written in the fields, by farmers who finally see, decide, negotiate and win. With their intelligence comes the nation's stability.

The author is an assistant professor at North South University and member of UNESCO AI Ethics Experts Without Borders.​

 

[Saif]

Are we giving up thinking to AI?

There has long been a fear that new technologies weaken human abilities. Socrates worried that writing would erode memory.

www.thedailystar.net

Are we giving up thinking to AI?

There has long been a fear that new technologies weaken human abilities. Socrates worried that writing would erode memory. Centuries later, people believed calculators would destroy mathematical skills, and many feared that the internet would ruin our ability to concentrate. Yet the concern surrounding artificial intelligence (AI) feels different. With AI now deeply integrated into everyday life, from homework help to professional decision-making, the question has gained new urgency: are we losing the art of thinking in the age of AI?

Across classrooms, workplaces and homes, AI has become both a convenient assistant and an almost invisible companion. Students routinely use AI tools to summarise chapters, draft essays and generate entire assignments. In universities, professors also rely on AI to design assessments, prepare teaching material and streamline administrative tasks. This has quietly become an everyday reality.

AI can make knowledge more accessible, support learners who struggle and increase efficiency. But beneath the optimism lies an uncomfortable possibility. As machines grow more capable, human cognitive skills may diminish. When the first instinct is to ask a model for answers, abilities related to analysis and reflection weaken through lack of use. Over time, this may reshape not only how we learn but how we think.

Children are especially vulnerable to this shift. Cognitive development grows out of doing hard things, working through problems, forming arguments and dealing with ambiguity. These experiences are the foundation of intellectual maturity. If AI is introduced too early or used too heavily, children risk skipping the stages that cultivate creativity, resilience and critical thinking.

In universities, the core issue is integrity. If students allow AI to complete assignments without engaging with the ideas, their degrees no longer reflect real learning. If lecturers depend too much on AI to prepare course content, academic quality and originality may decline. The danger is not that AI will replace teachers or learners. The danger is that AI may replace the thinking that teachers and learners need to do.

This does not mean we should turn away from AI. A more useful question is how to ensure that AI strengthens rather than weakens human intelligence.

The answer lies in our approach.

First, assessments need to be rethought. Traditional take-home essays and predictable problem sets no longer serve their purpose when AI can generate flawless responses. Educators need to emphasise in-class reasoning, oral examinations, real-world projects and reflective writing, formats where thinking cannot be outsourced.

Second, we must teach AI literacy, not only how to use AI but also how to question it. Students should learn to critique AI-generated material, identify inaccuracies and recognise bias. In this way, AI becomes a stimulus for critical thought rather than a replacement for it.

Third, we must reinforce the idea of AI as a tool rather than a crutch. A calculator did not remove the need to understand arithmetic. It allowed mathematicians to move forward. In a similar way, AI can free people from repetitive tasks so that they can focus on creativity, strategy and innovation. This benefit appears only when AI use remains mindful, like a tutor who guides rather than a service that completes the task.

We should avoid framing AI as a dire threat or a future tyrant. The real risk lies not in machines taking control but in humans voluntarily surrendering their own thinking. Intelligence is more than the ability to produce answers. It involves judgement, interpretation and understanding. These are human qualities, and they sharpen only with use.

AI will continue to evolve. Its power will increase. But the future of human cognition is limitless. If societies design educational and cultural systems with care, AI can elevate human thought rather than diminish it. Parents can support this by modelling mindful use and encouraging independent thinking.

The challenge before us is simple but profound: to use machines without allowing machines to use us.

The writer is chairman of Unilever Consumer Care Ltd​

 

[Saif]

Mobile technology fuels AI adoption in Bangladesh, Telenor Asia study finds

Mobile phones are at the heart of Bangladesh’s digital transformation, from accessing online learning and financial services to managing daily tasks and staying informed, according to the latest Telenor Asia study. Increasingly, these everyday conveniences are powered by artificial intellige

thefinancialexpress.com.bd

Mobile technology fuels AI adoption in Bangladesh, Telenor Asia study finds

bdnews24.com
Published :
Dec 09, 2025 14:38
Updated :
Dec 09, 2025 14:38

Mobile phones are at the heart of Bangladesh’s digital transformation, from accessing online learning and financial services to managing daily tasks and staying informed, according to the latest Telenor Asia study.

Increasingly, these everyday conveniences are powered by artificial intelligence, a technology that is rapidly reshaping how people live, work, and interact, the report said.

The "Digital Lives Decoded 2025: Building Trust in Bangladesh’s AI Future" report, launched on Tuesday, revealed that about 96 percent of Bangladeshi internet users now report regularly using AI, marking an increase from 88 percent in 2024.

The fourth edition of the study, based on a survey of 1,000 internet users, explores the emergence of AI in Bangladesh and underscores the vital need for responsible, ethical, and safe AI adoption.

Telenor Asia chief Jon Omund said: “As mobile phones continue to transform daily life in Bangladesh, they have become powerful enablers of smarter, more connected communities. With the increasing everyday adoption of AI, telecom operators have a unique opportunity and responsibility to build the secure digital infrastructure that underpins trustworthy AI.”

He stressed that connectivity is the foundation, and trust must be built into every layer. "Telenor Asia remains committed to supporting Bangladesh's digital journey and ensuring that the benefits of mobile technology are accessible to all in a safe and secure way.”

The key findings from the report are as follows:

‘MOBILES ENABLE SMARTER LIVES, BRING AI INTO DAILY REALITY’

Mobile technology is reshaping daily life by enabling online learning (62 percent), remote work (54 percent), and financial management (50 percent). Over the past year, remote work has seen the biggest increase in mobile-based use, rising by 39 percent, followed by budgeting and expense tracking, which grew by 36 percent.

Generational differences are also evident. Millennials are more likely than others to recognise the value of AI-driven features, such as smart home management, health tracking, and voice assistants. These trends indicate that expanding mobile use is driving everyday AI adoption as people increasingly embrace the benefits it brings.

‘TRUST IN AI TRANSLATES INTO OPTIMISM ON EDUCATION, ECONOMY’

Almost 6 in 10 in Bangladesh now use some form of AI every day. Many are now turning to AI to assist in creating content for school, work or personal use, as well as looking for personalised advice in health, finances or planning new experiences.

The growing use of AI has been especially pronounced at work, in daily activities and online shopping, pointing to its deepening role in day-to-day routines.

People are especially trusting of AI-generated educational content and AI chatbots, and this trust translates into an optimism around AI’s impact on education and the country’s economy.

‘AN UNTAPPED OPPORTUNITY FOR AI IN WORKPLACE’

AI usage at the workplace jumped from 44 percent to 62 percent in 2025. Yet only half of those using AI at work report that their organisations have a formal AI strategy, signalling room for greater institutional guidance on how this technology can be responsibly deployed.

Writing and creating content at work is one of the top use cases for AI, but there are more opportunities to encourage professionals to use AI to increase their productivity.

Currently, only 28 percent use it to delegate mundane or administrative tasks at work. Greater awareness around the different applications of AI could help increase effective adoption in the workplace.

‘YOUNGER GENERATIONS VOICE CONCERNS’

Even as people embrace AI tools in their daily lives, concerns have emerged around personal over-reliance on AI, lack of job security and privacy issues.

Younger generations are the most likely to use AI multiple times a day and to describe themselves as comfortable or even experts in their use of the technology.

Yet, they are also the most likely to voice concerns about the pace of development.

This combination of optimism and caution reflects a population eager to embrace AI while demanding safeguards.

Jon Omund concluded, “Alongside the optimism surrounding the potential of AI in Bangladesh, there is a pressing reality. As technology advances at pace, ensuring that everyone is connected and equipped to use these tools safely and effectively has never been more critical.

“Without access to connectivity or the skills to safely navigate the digital world, people are excluded from the digital ecosystem or left behind from the progress and opportunities that AI can enable. Our collective responsibility remains: continue working to bridge this divide and create a digital society where no one is left behind.”​

 

[Saif]

Will OpenAI be the next tech giant or next Netscape?

Three years after ChatGPT made OpenAI the leader in artificial intelligence and a household name, rivals have closed the gap and some investors are wondering if the sensation has the wherewithal to stay dominant.

www.thedailystar.net

Will OpenAI be the next tech giant or next Netscape?

Three years after ChatGPT made OpenAI the leader in artificial intelligence and a household name, rivals have closed the gap and some investors are wondering if the sensation has the wherewithal to stay dominant.

Investor Michael Burry, made famous in the film "The Big Short," recently likened OpenAI to Netscape, which ruled the web browser market in the mid-1990s only to lose to Microsoft's Internet Explorer.

"OpenAI is the next Netscape, doomed and hemorrhaging cash," Burry said recently in a post on X, formerly Twitter.

Researcher Gary Marcus, known for being skeptical of AI hype, sees OpenAI as having lost the lead it captured with the launch of ChatGPT in November 2022.

The startup is "burning billions of dollars a month," Marcus said of OpenAI.

"Given how long the writing has been on the wall, I can only shake my head" as it falls.

Yet ChatGPT was a tech launch like no other, breaking all consumer product growth records and now boasting more than 800 million -- paid subscription and unpaid -- weekly users.

OpenAI's valuation has soared to $500 billion in funding rounds, higher than any other private company.

But the ChatGPT maker will end this year with a loss of several billion dollars and does not expect to be profitable before 2029, an eternity in the fast-moving and uncertain world of AI.

Nonetheless, the startup has committed to paying more than $1.4 trillion to computer chip makers and data center builders to build infrastructure it needs for AI.

The fierce cash burn is raising questions, especially since Google claims some 650 million people use its Gemini AI monthly and the tech giant has massive online ad revenue to back its spending on technology.

Rivals Amazon, Meta and OpenAI-investor Microsoft have deep pockets the ChatGPT-maker cannot match.

A charismatic salesman, OpenAI chief executive Sam Altman flashed rare annoyance when asked about the startup's multi-trillion-dollar contracts in early November.

A few days later, he warned internally that the startup is likely to face a "turbulent environment" and an "unfavorable economic climate," particularly given competitive pressure from Google.

And when Google released its latest model to positive reactions, Altman issued a "red alert," urging OpenAI teams to give ChatGPT their best efforts.

OpenAI unveiled its latest ChatGPT model last week, that same day announcing Disney would invest in the startup and license characters for use in the bot and Sora video-generating tool.

OpenAI's challenge is inspiring the confidence that the large sums of money it is investing will pay off, according to Foundation Capital partner Ashu Garg.

For now OpenAI is raising money at lofty valuations while returns on those investments are questionable, Garg added.

Yet OpenAI still has the faith of the world's deepest-pocketed investors.

"I'm always expecting OpenAI's valuation to come down because competition is coming and its capital structure is so obviously inappropriate," said Pluris Valuation Advisors president Espen Robak.

"But it only seems to be going up."

Opinions are mixed on whether the situation will result in OpenAI postponing becoming a publicly traded company or instead make its way faster to Wall Street to cash in on the AI euphoria.

Few AI industry analysts expect OpenAI to implode completely, since there is room in the market for several models to thrive.

"At the end of the day, it's not winner take all," said CFRA analyst Angelo Zino.

"All of these companies will take a piece of the pie, and the pie continues to get bigger," he said of AI industry frontrunners.

Also factored in is that while OpenAI has made dizzying financial commitments, terms of deals tend to be flexible and Microsoft is a major backer of the startup.​

 

[Saif]

Ethical AI no longer optional

FROM mobile banking to public services, artificial intelligence is quietly shaping decisions that affect millions of lives in Bangladesh and beyond. These systems promise speed and efficiency and in many cases they deliver exactly that. Yet without proper governance, they can also exclude...

www.newagebd.net

Ethical AI no longer optional

FROM mobile banking to public services, artificial intelligence is quietly shaping decisions that affect millions of lives in Bangladesh and beyond. These systems promise speed and efficiency and in many cases they deliver exactly that. Yet without proper governance, they can also exclude, discriminate and erode public trust. As AI adoption accelerates across sectors, ethical use is an immediate necessity. The scale and speed of these tools are unprecedented. But alongside these gains comes a fundamental question: who is accountable when machines influence decisions that directly affect people’s lives?

Using AI ethically means ensuring that technology serves people, not the other way around. In healthcare, AI can assist in detecting diseases earlier and improving treatment outcomes. Yet when systems are trained on biased or incomplete data, certain patients may be misdiagnosed, overlooked or excluded. In education, AI can personalise learning pathways, but without safeguards it can compromise student privacy or widen existing inequalities between those with access to high-quality digital tools and those without.

These risks are no longer hypothetical. The consequences of unethical AI use are already visible. Facial recognition technologies, now deployed by law enforcement and public agencies in many countries, have repeatedly been shown to misidentify people with darker skin tones at higher rates. In documented cases, innocent individuals have been questioned, detained or publicly embarrassed because an algorithm produced a false match. The technology functioned as designed, but the absence of ethical oversight turned efficiency into injustice.

Automated hiring systems provide another warning. Some AI-powered recruitment tools were trained on historical data reflecting past hiring patterns that favoured certain genders, institutions or social backgrounds. As a result, qualified candidates were filtered out silently, never knowing why their applications failed. When discrimination is embedded inside an algorithm, it becomes harder to detect, challenge or correct. Bias does not disappear through automation; it becomes less visible and more difficult to contest.

Financial services perhaps illustrate the stakes most clearly. AI-driven credit scoring and risk assessment systems increasingly decide who can access loans, credit cards or even basic banking services. In many cases, people are denied support without any clear explanation or opportunity to appeal. For small business owners, informal workers and low-income households, such decisions can determine whether they move forward or fall behind. This is a failure of governance.

The issue is not artificial intelligence itself, but the lack of clear guardrails around its use. This is where AI governance becomes critical. In practical terms, governance means deciding who is responsible for automated decisions, how those decisions are reviewed and what happens when systems cause harm. It requires testing for bias, ensuring decisions can be explained in plain language and keeping humans accountable, especially when AI affects livelihoods, rights or access to essential services.

When governance is done well, AI stops being an untouchable ‘black box’ and becomes something people can question and trust. In healthcare, it ensures doctors, not algorithms, make final clinical decisions. In banking, it allows customers to understand and challenge automated credit assessments. In public services, it means citizens are informed when algorithms are used and are given a clear route to appeal outcomes.

Bangladesh offers a timely example of why this matters. As digital financial services expand rapidly, banks and financial institutions are increasingly relying on artificial intelligence and advanced analytics for fraud detection, transaction monitoring, customer profiling and credit assessment. Automation is unavoidable at this scale. However, discussions and research initiatives led by the Bangladesh Institute of Bank Management have repeatedly highlighted a gap: while the use of data analytics and AI is growing across the banking sector, governance, explainability and customer awareness have not always kept pace. The need for stronger internal policies, clearer accountability and closer alignment with regulatory expectations has been consistently emphasised.

For banks, good AI governance can begin with practical steps. Institutions should develop clear internal policies defining where AI can be used, where it should not be used and which decisions must always involve human review such as loan rejections, account freezes or high-risk customer classifications. Customers should receive plain-language explanations for automated decisions, along with simple and accessible appeal mechanisms. Regular model validation, bias testing and documentation should be treated as standard risk controls, no different from credit, operational or cybersecurity risk management.

These measures are not separate from regulation. They align closely with existing expectations set by Bangladesh Bank, which already emphasises strong risk management, customer protection, internal controls, data security, model validation and information and communication technology governance. Ethical AI governance complements current laws and supervisory frameworks, including data protection obligations and consumer protection requirements. Rather than adding complexity, it can be integrated into existing compliance processes.

The same principles apply beyond banking. In healthcare, AI tools should support clinical judgement, not replace it. In education, student data must be protected and automated assessments must be fair, explainable and open to review. In public services, transparency is essential. Citizens should know when algorithms influence decisions and should have the right to question outcomes that affect their access to services or opportunities.

Effective AI governance does not require massive investment or complex systems. Many safeguards are simple. Organisations can require human oversight for high-impact decisions, conduct regular bias and impact checks and provide clear explanations for automated outcomes. Assigning clear responsibility through a designated AI or ethics lead ensures accountability remains with people, not systems. In some countries, public agencies now maintain algorithm registers that openly list where automated decision-making is used, significantly improving public confidence.

Internationally, momentum is building. Bodies such as the Organisation for Economic Co-operation and Development and the United Nations Educational, Scientific and Cultural Organization have developed principles centred on fairness, transparency and human oversight. The Institute of Electrical and Electronics Engineers has issued ethical guidelines for responsible AI design. In Europe, the General Data Protection Regulation protects individuals from harmful automated decision-making and misuse of personal data. More recently, the ISO/IEC 42001 standard has provided organisations with a structured way to manage AI risks, similar to approaches used for financial or cybersecurity risks.

AI is advancing faster than laws, institutions and public awareness can adapt. Without effective governance, weak oversight risks deepening inequality, damaging trust and undermining the benefits of digital transformation. Governments must balance innovation with protection. Organisations must embed ethical thinking into AI systems from the outset, not as an afterthought. Professionals must remember that AI is a tool, not a substitute for human judgement. Oversight, accountability and common sense still matter. With strong governance, transparency and shared values, AI can serve society supporting inclusion, strengthening trust and delivering real benefits across Bangladesh and beyond.

BM Zahid ul Haque is an information security officer.​

 

[Saif]

Announcing AI Protection: Security for the AI era

As AI use increases, security remains a top concern, and we often hear that organisations are worried about risks that can come with rapid adoption. Google Cloud is committed to helping our customers confidently build and deploy AI in a secure, compliant, and private manner.

en.prothomalo.com

Announcing AI Protection: Security for the AI era

Archana Ramamoorthy
Published: 14 Dec 2025, 19: 49

As AI use increases, security remains a top concern, and we often hear that organisations are worried about risks that can come with rapid adoption. Google Cloud is committed to helping our customers confidently build and deploy AI in a secure, compliant, and private manner.

Today, we’re introducing a new solution that can help you mitigate risk throughout the AI lifecycle. We are excited to announce AI Protection, a set of capabilities designed to safeguard AI workloads and data across clouds and models — irrespective of the platforms you choose to use.

AI Protection helps teams comprehensively manage AI risk by:
​

• Discovering AI inventory in your environment and assessing it for potential vulnerabilities​
• Securing AI assets with controls, policies, and guardrails​
• Managing threats against AI systems with detection, investigation, and response capabilities​

AI Protection is integrated with Security Command Center (SCC), our multicloud risk-management platform, so that security teams can get a centralised view of their AI posture and manage AI risks holistically in context with their other cloud risks.

AI Protection helps organisations discover AI inventory, secure AI assets, and manage

AI threats, and is integrated with Security Command Center.
AI Protection helps organisations discover AI inventory, secure AI assets, and manage AI threats, and is integrated with Security Command Center.

Discovering AI inventory

Effective AI risk management begins with a comprehensive understanding of where and how AI is used within your environment. Our capabilities help you automatically discover and catalog AI assets, including the use of models, applications, and data — and their relationships.

Understanding what data supports AI applications and how it’s currently protected is paramount. Sensitive Data Protection (SDP) now extends automated data discovery to Vertex AI datasets to help you understand data sensitivity and data types that make up training and tuning data. It can also generate data profiles that provide deeper insight into the type and sensitivity of your training data.

Once you know where sensitive data exists, AI Protection can use Security Command Center’s virtual red teaming to identify AI-related toxic combinations and potential paths that threat actors could take to compromise this critical data, and recommend steps to remediate vulnerabilities and make posture adjustments.

Securing AI assets

Model Armor, a core capability of AI Protection, is now generally available. It guards against prompt injection, jailbreak, data loss, malicious URLs, and offensive content. Model Armor can support a broad range of models across multiple clouds, so customers get consistent protection for the models and platforms they want to use — even if that changes in the future.

Model Armor provides multi-model, multicloud support for generative AI applications.

Today, developers can easily integrate Model Armor’s prompt and response screening into applications using a REST API or through an integration with Apigee. The ability to deploy Model Armor in-line without making any app changes is coming soon through integrations with Vertex AI and our Cloud Networking products.

"We are using Model Armor not only because it provides robust protection against prompt injections, jailbreaks, and sensitive data leaks, but also because we're getting a unified security posture from Security Command Center. We can quickly identify, prioritise, and respond to potential vulnerabilities — without impacting the experience of our development teams or the apps themselves. We view Model Armor as critical to safeguarding our AI applications and being able to centralise the monitoring of AI security threats alongside our other security findings within SCC is a game-changer," said Jay DePaul, chief cybersecurity and technology risk officer, Dun and Bradstreet.

Organisations can use AI Protection to strengthen the security of Vertex AI applications by applying postures in Security Command Center. These posture controls, designed with first-party knowledge of the Vertex AI architecture, define secure resource configurations and help organisations prevent drift or unauthorised changes.

Managing AI threats

AI Protection operationalises security intelligence and research from Google and Mandiant to help defend your AI systems. Detectors in Security Command Center can be used to uncover initial access attempts, privilege escalation, and persistence attempts for AI workloads. New detectors to AI Protection based on the latest frontline intelligence to help identify and manage runtime threats such as foundational model hijacking are coming soon.

"As AI-driven solutions become increasingly commonplace, securing AI systems is paramount and surpasses basic data protection. AI security — by its nature — necessitates a holistic strategy that includes model integrity, data provenance, compliance, and robust governance,” said Dr. Grace Trinidad, research director, IDC.

“Piecemeal solutions can leave and have left critical vulnerabilities exposed, rendering organisations susceptible to threats like adversarial attacks or data poisoning, and added to the overwhelm experienced by security teams. A comprehensive, lifecycle-focused approach allows organisations to effectively mitigate the multi-faceted risks surfaced by generative AI, as well as manage increasingly expanding security workloads. By taking a holistic approach to AI protection, Google Cloud simplifies and thus improves the experience of securing AI for customers," she said.

Complement AI Protection with frontline expertise

The Mandiant AI Security Consulting Portfolio offers services to help organisations assess and implement robust security measures for AI systems across clouds and platforms. Consultants can evaluate the end-to-end security of AI implementations and recommend opportunities to harden AI systems. We also provide red teaming for AI, informed by the latest attacks on AI services seen in frontline engagements.

Building on a secure foundation

Customers can also benefit from using Google Cloud’s infrastructure for building and running AI workloads. Our secure-by-design, secure-by-default cloud platform is built with multiple layers of safeguards, encryption, and rigorous software supply chain controls.

For customers whose AI workloads are subject to regulation, we offer Assured Workloads to easily create controlled environments with strict policy guardrails that enforce controls such as data residency and customer-managed encryption. Audit Manager can produce evidence of regulatory and emerging AI standards compliance. Confidential Computing can help ensure data remains protected throughout the entire processing pipeline, reducing the risk of unauthorised access, even by privileged users or malicious actors within the system.

Additionally, for organisations looking to discover unsanctioned use of AI, or shadow AI, in their workforce, Chrome Enterprise Premium can provide visibility into end-user activity as well as prevent accidental and intentional exfiltration of sensitive data in gen AI applications.

*Archana Ramamoorthy is the senior director of product management at Google Cloud Security.​

 

[Saif]

Impact of AI on apparel sector

THE apparel sector is the backbone of the economy, employing more than 4.4 million people and accounting for more than 80 per cent of the export earnings. For decades, its competitive edge has rested on abundant, low-cost labour. Today, that model is facing its most significant transformation...

www.newagebd.net

Impact of AI on apparel sector

The apparel work force, already lacking formal vocational training, is ill-equipped for new digital roles, writes Md Mamun Alam

THE apparel sector is the backbone of the economy, employing more than 4.4 million people and accounting for more than 80 per cent of the export earnings. For decades, its competitive edge has rested on abundant, low-cost labour. Today, that model is facing its most significant transformation, driven not by global trade wars or economic shifts but by a silent, rapidly evolving force that is artificial intelligence.

The adoption of artificial intelligence and automation, often categorised under the Fourth Industrial Revolution, is reshaping workforce productivity in apparel factories.

The integration of artificial intelligence primarily targets repetitive, high-volume and complex cognitive tasks previously susceptible to human errors. The most immediate productivity gains are visible in three critical areas:

Quality control: Traditionally a labour-intensive process, fabric and garment inspection is now revolutionised by computer vision systems. High-resolution cameras feeding data into artificial intelligence algorithms can automatically identify stitching mistakes, tears and stains in real time. Research suggests that artificial intelligence systems can achieve up to a 30 per cent boost in quality control efficiency, leading to a measurable reduction in manufacturing defects, sometimes as much as 20 per cent. The automated systems operate at a speed human inspectors cannot match, ensuring superior quality consistency demanded by fast-fashion global buyers.

Supply chain and planning: Artificial intelligence-driven predictive analytics is used to move beyond manual scheduling. Algorithms optimise production schedules based on real-time factors such as raw material availability, machine capacity and evolving global demand. This ability to forecast accurately minimises waste, controls inventory better and shortens the critical lead time for export orders, bolstering the competitiveness of manufacturers against rivals in Vietnam.

Operational efficiency: Artificial intelligence-powered tools also enhance overall factory flow through predictive maintenance, identifying equipment failures before they happen and reducing costly downtime. Furthermore, robotic process automation handles monotonous tasks like material sorting and handling, freeing up human workers to focus on tasks requiring dexterity and judgment.

While productivity soars, the ethical and social implications for the vast apparel work force are profound. Aartificial intelligence is a double-edged sword, threatening millions of jobs, especially among the predominantly low-skilled female workers who constitute the majority of the sector’s employees.

Studies estimate that up to 60 per cent of the current apparel work force, about 2.7 million people, could be displaced by 2041 by automation. This is not a distant threat. Recent industry reports indicate that technological upgrades have already led to a reduction in the overall factory employment by around 30 per cent, primarily affecting entry-level positions such as helpers. Production stages involving highly repetitive work such as cutting have seen the steepest labour cuts.

The key to navigating this transition is recognising that artificial intelligence does not eliminate all jobs. It replaces tasks and creates new, higher-value roles. The demand is shifting from manual operators to specialists who can interact with, maintain and manage intelligent systems: artificial intelligence operators, robot technicians and data analysts.

The core challenge that Bangladesh today faces is the mismatch of skills. The apparel work force, already lacking formal vocational training, is ill-equipped for these new digital roles. With limited training and development opportunities available within the factory system, a significant portion of the displaced work force risks being left behind.

A strategic, unified national response is mandatory. While the government’s national industry policy recognises artificial intelligence as a transformative technology, explicit policies surrounding job loss compensation, reskilling mandates and support for a ‘just transition’ remain critically underdeveloped.

Bangladesh must move beyond viewing its work force as a cheap, disposable commodity. The success of the apparel sector depends on strategic government intervention through subsidies for artificial intelligence adoption, coupled with compulsory, subsidised reskilling and upskilling programmes.

By transforming factory workers into ‘technicians’ and ‘supervisors’ equipped with digital literacy, Bangladesh can retain its human capital, ensure a smoother transition and solidify its position in the global high-value apparel market. Failure to do so risks trading short-term productivity gains for long-term social instability.

Md Mamun Alam is chief operating officer, C-NET.​

 

[Saif]

Tiny tech, big AI power: what are 2-nanometre chips?

Taiwan’s world-leading microchip manufacturer TSMC says it has started mass producing next-generation ‘2-nanometre’ chips...

www.newagebd.net

Tiny tech, big AI power: what are 2-nanometre chips?
Agence France-Presse . Tokyo 31 December, 2025, 23:04

Taiwan’s world-leading microchip manufacturer TSMC says it has started mass producing next-generation ‘2-nanometre’ chips.

AFP looks at what that means, and why it’s important:

The computing power of chips has increased dramatically over the decades as makers cram them with more microscopic electronic components.

That has brought huge technological leaps to everything from smartphones to cars, as well as the advent of artificial intelligence tools like ChatGPT.

Advanced 2-nanometre (2nm) chips perform better and are more energy-efficient than past types, and are structured differently to house even more of the key components known as transistors.

The new chip technology will help speed up laptops, reduce data centres’ carbon footprint and allow self-driving cars to spot objects quicker, according to US computing giant IBM.

For artificial intelligence, ‘this benefits both consumer devices -- enabling faster, more capable on-device AI -- and data centre AI chips, which can run large models more efficiently’, said Jan Frederik Slijkerman, senior sector strategist at Dutch bank ING.

Producing 2nm chips, the most cutting-edge in the industry, is ‘extremely hard and expensive’, requiring ‘advanced lithography machines, deep knowledge of the production process, and huge investments’, Slijkerman told AFP.

Only a few companies are able to do it: TSMC, which dominates the chip manufacturing industry, as well as South Korea’s Samsung and US firm Intel.

TSMC is in the lead, with the other two ‘still in the stage of improving yield’ and lacking large-scale customers, said TrendForce analyst Joanne Chiao.

Japanese chipmaker Rapidus is also building a plant in northern Japan to make 2nm chips, with mass production slated for 2027.

TSMC’s path to mass 2nm production has not always been smooth.

Taiwanese prosecutors charged three people in August with stealing trade secrets related to 2nm chips to help Tokyo Electron, a Japanese company that makes equipment for TSMC.

‘This case involves critical national core technologies vital to Taiwan’s industrial lifeline,’ the high prosecutors’ office said at the time.

Geopolitical factors and trade wars are also at play.

Nikkei Asia reported this summer that TSMC, which counts Nvidia and Apple among its clients, will not use Chinese chipmaking equipment in its 2nm production lines to avoid disruption from potential US restrictions.

TSMC says they plan to speed up production of 2nm chips in the United States, currently targeted for ‘the end of the decade’.

Extremely tiny -- for reference, an atom is approximately 0.1 nanometres across.

But in fact 2nm does not refer to the actual size of the chip itself, or any chip components, and is just a marketing term.

Instead ‘the smaller the number, the higher the density’ of these components, Chiao told AFP.

IBM says 2nm designs can fit up to 50 billion transistors, tiny components smaller than a virus, on a chip the size of a fingernail.

To create the transistors, slices of silicon are etched, treated and combined with thin films of other materials.

A higher density of transistors results in a smaller chip or one the same size with faster processing power.

Yes, and TSMC is already developing ‘1.4-nanometre’ technology, reportedly to go into mass production around 2028, with Samsung and Intel not far behind.

TSMC started high-volume 3nm production in 2023, and Taiwanese media says the company is already building a 1.4nm chip factory in the city of Taichung.

As for 2nm chips, Japan’s Rapidus says they are ‘ideal for AI servers’ and will ‘become the cornerstone of the next-generation digital infrastructure’, despite the huge technical challenges and costs involved.​

 

[Saif]

Proactive steps can future-proof jobs in the AI era

There is a widespread fear across US campuses, businesses, and industries that the emergence of artificial intelligence (AI) and its rapid integration into all spheres of our lives will lead to job losses and a lifestyle where robots control our daily routines.

www.thedailystar.net

Proactive steps can future-proof jobs in the AI era

18 January 2026, 12:00 PM
By Dr Abdullah Shibli

FILE VISUAL: REUTERS

There is a widespread fear across US campuses, businesses, and industries that the emergence of artificial intelligence (AI) and its rapid integration into all spheres of our lives will lead to job losses and a lifestyle where robots control our daily routines. However, further exploration of this notion shows that much of this projection is exaggerated, fed by our fascination with dystopian science fiction. In reality, AI will gradually make inroads into transportation, manufacturing, and agriculture; AI-driven robotics will replace routine tasks, provide health and other services, thus boosting financial growth.

My commentary is for the younger generation considering their career paths, today’s industry leaders evaluating investment opportunities for the future, and thought leaders in academia and AI research.

Let us take stock of the current situation in the US and other advanced countries. AI is poised to eliminate a variety of jobs, particularly those involving routine tasks, data analysis, and customer service, while roles requiring human judgement and emotional intelligence are likely to remain safe.

Microsoft developed an “AI applicability score” to measure how well AI can perform the core tasks of various jobs. Jobs with high scores are more likely to be transformed or replaced by AI technologies. Many of these jobs are in domains such as computer and mathematical fields, office and administrative support, and sales.

In a recent article in The New York Times, Sal Khan, the Bangladeshi founder of Khan Academy, wrote, “I believe artificial intelligence will displace workers at a scale many people don’t yet realize.” However, he also calls on business leaders to invest in retraining workers to adapt to new workplace technologies.

AI-enhanced humanoid robots and autonomous machines will be in high demand across warehouses, supply chains, transportation, and agriculture. In Bangladesh, many manual tasks, including irrigation, tilling, and fertiliser application, have seen the intrusion of mechanisation for decades.

Robotics in agriculture, often referred to as “agribots,” encompasses a range of automated technologies designed to improve farming practices. These robots perform essential tasks such as planting, harvesting, monitoring crop health, and managing livestock, significantly enhancing productivity and efficiency in modern agriculture.

While AI will enable us to work and learn better, there will be a growing need for skilled labour to operate and manage this new technology. The challenge our society, particularly our new generation, faces is adapting to the changing job market and economic ecosystem. Universities and industry leaders will now need to have a subcommittee to study and better understand the societal and economic impact of AI.

Microsoft itself is dedicating some energy to understanding the future of the workplace. It is providing considerable funding to graduate students engaged in research, market design, the economics of Artificial Intelligence (AI), economics and computation, social learning, applied microeconomics, microeconomic theory, and behavioural economics.

Sal Khan advised big companies to invest one percent of their profits in training their own workers to adapt to AI and robotics. He estimates that one percent of the combined profit of a dozen of the world’s largest corporations would create a $10 billion annual fund.

Now, let us turn to retraining the workers who will undoubtedly be affected by changes in the job market. The revolution brought about by AI and automation is fuelled by chipmakers such as Nvidia. In a recent interview, Jensen Huang, the founder and CEO of Nvidia, said that polls show AI-driven robots will be used for office and administrative support, domestic work, and repetitive tasks. However, robots will not be able to outperform humans in healthcare, classroom teaching, and scores of other jobs.

The US Bureau of Labor Statistics projects that nearly two million jobs will open up annually in the healthcare sector during the next decade. UNESCO estimates a global shortage of 44 million teachers by 2030. In the US, the construction industry needs more than 500,000 additional workers annually just to meet demand; meanwhile, openings for electricians and plumbers are growing faster than average. The hospitality and elder care industries—work rooted in empathy and human presence—are expanding, not shrinking.

Our teachers must adapt to the changing times and prepare our students for smart jobs. With advances in AI technology, many jobs will undergo transformation, and some roles will face a significant risk of automation. Workers in knowledge-intensive fields should be proactive in adapting to these changes, seeking opportunities for reskilling and embracing new technologies to remain competitive in the evolving job market.

At the 1 Billion Followers Summit 2026 in Dubai, educators, content creators, and learning designers called for a fundamental shift in how education is delivered. Education must pivot from rote learning to skill-based development (problem-solving, digital literacy) to equip graduates for this evolving landscape, focusing less on degrees and more on practical workplace readiness, as AI displaces some roles while boosting productivity.

Turning to agriculture, it is a sector that is crucial in meeting the food demand for a growing population projected to reach 9.7 billion people by 2050. Robots can operate continuously, performing tasks faster and more accurately than human labour, which is in short supply anyway. While the integration of robotics in agriculture presents numerous advantages, challenges such as high initial costs and the need for skilled operators exist. Any potential job displacement can be addressed by strengthening the skilled crafts and trades required to sustain the AI-driven economy.

The goal of universities should be to move away from a mindset focused solely on producing more graduates and towards a system that equips students with workplace skills. To do that, educational reform must transition immediately from exam-based assessment to skill-based learning.

Dr Abdullah Shibli is an economist and academic, currently working with a non-profit fiscal intermediary in Boston, US. He previously worked for the World Bank and Harvard University.​

 

[Saif]

Staying human in the age of AI

AI has slipped into daily life with a kind of stealth. One moment you are using it to tidy up an email or translate a paragraph, and the next you are letting it outline your presentation, draft your report, suggest your next move, even tell you what you feel.

www.thedailystar.net

Staying human in the age of AI

By Nadia Jahan

Image: Esma Melike Sezer/ Unsplash

AI has slipped into daily life with a kind of stealth. One moment you are using it to tidy up an email or translate a paragraph, and the next you are letting it outline your presentation, draft your report, suggest your next move, even tell you what you feel. The shift is not just about new software. It is about habits. In a country where young people are under relentless pressure to compete, save time and sound polished, the temptation is obvious: delegate as much as possible, move faster than everyone else, and let the machine take the strain.

But there is a cost to handing over too much. The more we outsource, the more we risk hollowing out the very qualities that make us employable, resilient, and alive to one another. Staying human in the age of AI means knowing when to use the tool and when to step back from it, not out of nostalgia, but because some parts of life only work when we do them ourselves.

There is an easy misunderstanding about AI that makes over-delegation feel harmless. We treat it like a calculator for words, a neutral device that simply speeds up what we already know. Yet many AI systems do more than compute. They generate. They suggest. They complete our thoughts for us, often in a tone that sounds confident and coherent. That can create the illusion of competence even when the underlying thinking is thin. If we accept that illusion too often, we begin to live in a world where sounding right matters more than being right, and where the first draft becomes the final one.

Image: Giingerann/ Unsplash

The first thing we lose is the muscle of judgement. Writing a message, shaping an argument, or making a decision is not only about producing an output. It is about weighing what matters, anticipating how it will land, and taking responsibility for the consequences. When you let AI do the heavy lifting every time, you may still get something workable on the page, but you gradually weaken the inner sense that tells you what is true, what is fair, what is missing, and what does not sound like you. That sense is slow to build and easy to erode.

There is also a practical risk: dependency makes people fragile. AI tools can be wrong, inconsistent, or strangely generic. They can flatten nuance, misunderstand context, and reproduce patterns that are common rather than correct. If you have not practised doing the work yourself, you cannot reliably catch the errors. You also struggle when the stakes rise: when a client challenges a claim, when an interviewer asks you to explain your reasoning, when you have to negotiate, persuade, or improvise in real time. In those moments, there is no prompt that can replace a well-trained mind.

The second thing we lose is originality. Not in the grand sense of artistic genius, but in the everyday sense that your work carries a trace of your experience: your curiosity, your humour, your way of seeing. AI can imitate styles and remix familiar patterns, which is exactly why it can be useful for routine tasks. But if you let it write everything, you end up speaking in borrowed rhythms. You become less memorable. You become easier to replace.

This is where the so-called “human touch” becomes more than a sentimental phrase. In competitive workplaces and crowded markets, the human touch is often the differentiator.


It is the ability to listen properly to what someone is asking, to sense what they are not saying, to respond in a way that makes them feel understood rather than processed. It is empathy, timing, judgement, tact. It is also taste: knowing what to leave out, when to simplify, when to insist on complexity, when to be firm, when to be kind. AI can help with drafts and options, but it cannot fully replace the lived intelligence that comes from being in the world, paying attention, and caring about consequences.

In Bangladesh, this matters because so much opportunity depends on relationships. Whether you are pitching a client, working in a team, running a small business, freelancing online, or building a startup, trust is the currency. Trust grows through consistency and human presence. It grows when you show up, reply thoughtfully, keep your word, and treat people as people. If AI encourages a culture of shortcuts where every message is a template and every interaction is optimised for speed, trust becomes harder to earn. You might respond faster, but you can sound less real.

The deeper danger is that over-delegation does not stop at work. It creeps into the personal. When people use AI to avoid awkward conversations, to manage emotions, to write apologies, to craft romantic messages, to mediate conflicts, they may feel relief in the moment. But avoidance has a price. Relationships are not built through perfect phrasing. They are built through vulnerability, patience, and the willingness to sit with discomfort. If you outsource the difficult parts of being with other people, you do not develop the skills that make intimacy possible.

That is why social skills are not a soft extra in the age of AI. They are a survival skill. As machines get better at routine cognitive output, what remains valuable is what machines cannot do in the same way: build rapport, read a room, resolve conflict, motivate a team, mentor someone younger, earn a customer’s loyalty, handle criticism without collapsing, and communicate under pressure. These skills have always mattered. Now they matter more, because they are harder to automate and because they protect us from turning ourselves into something machine-like.

Image: Michaelle Daoust/ Unsplash

The irony is that technology often makes social skills feel optional. When you can text instead of call, when you can order without speaking, when you can work remotely and never meet your colleagues, you can go through days with minimal human friction. AI takes this further by offering a substitute for interaction: an entity that always responds, never gets tired, and rarely pushes back. If we are not careful, we start to prefer that frictionless exchange to real relationships, which are messy and demanding. Over time, the preference becomes a habit, and the habit becomes a way of life.

This is how we risk mechanising ourselves. Not because machines become human, but because humans begin to adopt the machine’s logic. We optimise everything. We minimise effort. We reduce conversation to transactions. We treat people as obstacles or opportunities, not as complex beings. We choose the easiest route rather than the most meaningful one. When enough individuals do this, society becomes colder. Loneliness rises. Trust falls. Even success feels strangely thin.

Staying human, then, is partly a matter of deliberate resistance. It means choosing, again and again, to practise what AI makes easy to avoid.

It means writing sometimes without assistance, so you can hear your own voice and strengthen your ability to think through language. It means doing mental work slowly enough to understand it, rather than producing answers quickly enough to move on. It means reading deeply rather than skimming summaries, because attention is a form of respect, and because complex problems cannot be solved with shallow understanding.

It also means making extra efforts to protect human-to-human connection in a world that quietly erodes it. Call a friend instead of sending a perfectly composed message. Sit with someone in person even when it is inconvenient. Ask questions you cannot outsource. Listen without planning your next reply. Join communities that are not about productivity: sports clubs, volunteer groups, study circles, cultural events, neighbourhood networks. These are not distractions from the future. They are part of what makes any future worth living in.

For young people especially, there is a temptation to treat social skills as secondary to technical skills. Learn the tools, build the portfolio, collect the certificates, and the rest will follow. But the person who thrives in an AI-shaped economy will often be the one who can combine competence with connection. The future belongs to people who can use machines without becoming machine-like: who can collaborate across differences, communicate clearly, negotiate fairly, and keep a sense of purpose bigger than optimisation.

None of this requires rejecting AI. It requires putting it in its place. AI is best understood as an amplifier. Used wisely, it can amplify your learning, your productivity, your creativity. Used carelessly, it amplifies your laziness, your dependence, your isolation. The difference is not the tool. It is the human using it.

The point of staying human is not to prove you can do everything the hard way. It is to protect what only humans can do well: meaning-making, moral judgement, genuine care, solidarity, courage. These are not romantic ideals. They are practical advantages in a volatile world. They help people adapt, recover, cooperate, and build institutions that last.

In the coming years, Bangladesh’s young people will be told, repeatedly, that the future belongs to those who embrace AI. That is true, in a narrow sense. But the broader truth is that the future belongs to those who embrace people. The real challenge is not learning to prompt a machine. It is learning to remain fully human while you do.

Nadia Jahan is a development communications professional based out of Dhaka.​

 

[Saif]

How will artificial intelligence transform the labour market?

Artificial Intelligence (AI) is expected to pose the biggest disruption to global labour markets since the industrial revolution in the 19th century.

www.thedailystar.net

How will artificial intelligence transform the labour market?

 

[Saif]

AI is becoming a companion for some teens, and that’s a problem

Address AI addiction risks as teenagers increasingly use chatbots for emotional support over human connection. Recognize behavioural patterns like social withdrawal and prioritize offline interactions to ensure healthy psychological development and balanced digital life habits.

www.thedailystar.net

AI is becoming a companion for some teens, and that’s a problem
29 January 2026, 16:13 PM

By Ayman Anika

Photo: Collected / bertellifotografia / Pexels

Chatbots were originally designed to answer questions and help with tasks, but an unexpected trend is emerging: many teenagers are treating these systems as companions. What started as curiosity is turning into what experts describe as a form of AI addiction — not in the clinical sense yet, but as a pattern of reliance that can interfere with daily life.

This isn’t just about teenagers preferring technology over face-to-face interaction. It’s about the way AI is being used to fill emotional gaps. Teens are spending hours chatting with AI programs, not just for help with homework but for emotional support, encouragement, and social interaction. Some report talking to AI more than they talk to real friends.

The appeal is understandable. AI doesn’t judge, doesn’t interrupt, and is always available. For teens who feel misunderstood, lonely, or socially anxious, a chatbot can feel like a safe space.

Photo: Collected / pavel danilyuk / Pexels


Providers of these technologies design them to be responsive, engaging, and empathetic, which only strengthens the habit. A conversation that begins with a homework question can easily turn into an emotional outlet.

But that convenience comes with a cost. A 2024 survey linked to trends in AI use shows that when children lean on technology instead of people, it can affect emotional development, school performance, and real-world relationships.

Because chatbots can mimic empathy without actually experiencing it, they give teens a sense of connection that isn’t grounded in mutual understanding. That can make real relationships feel harder, or less rewarding.

Experts point out that habitual overuse of any technology — in this case AI — starts to look like more than just a habit. Patterns include prioritising chatbot interaction over friendships, hiding usage from loved ones, and becoming distressed when access is restricted. These behaviours mirror what psychologists recognise as problematic tech use, similar to excessive gaming or social media addiction.

Parents can play a crucial role in recognising this early. Signs include sudden mood shifts when screen time is limited, declining interest in offline activities, and frequently using AI late at night. Conversations about technology often focus on safety and screen time limits, but with AI, the discussion needs to include how and why teens are using it.


Opening a dialogue without judgment helps teens reflect on their own habits. Rather than banning AI outright, what matters more is helping them find balance and understanding the difference between supportive use and dependency.

Photo: Collected / bertellifotografia / Pexels


Setting clear limits and routines can help. Experts suggest designated tech-free hours, encouraging in-person interaction, and promoting hobbies unrelated to screens. Some families choose to check in daily about how technology made a teen feel — not just how long they used it. That shift from quantity to quality of use aligns technological tools with healthy emotional development.

There’s no simple fix. AI is here to stay, and for many it will be a useful resource. But when reliance on AI begins to replace real human connection or interferes with daily life, parents and caregivers need to pay attention. Helping teens navigate this new terrain is as much about emotional guidance as it is about digital rules.

AI doesn’t have to be the problem. Without awareness and limits, it can become a substitute for the support and connection that only real human relationships can provide.​

 

[Saif]

The fear and the future: what the emergence of AI can mean for our youth

The notion that artificial intelligence (AI) is going to rewire the basis of our understanding of many entities, even at this point in time, seems a little far-fetched.

www.thedailystar.net

The fear and the future: what the emergence of AI can mean for our youth
20 January 2026, 07:40 AM

Illustration: Zarif Faiaz

The notion that artificial intelligence (AI) is going to rewire the basis of our understanding of many entities, even at this point in time, seems a little far-fetched. Yet, what it does have the potential to do is lead us into new territory. As things stand, though, most users are still trying to figure out its limitations, while those who develop it seek to shatter them. In between the swinging tides stand the youth. Left in limbo, the young demographic is left wondering precisely how disruptive artificial intelligence really is and what its development means for their future.

Zunayed Ahmed, a third-year software engineering student at Seneca Polytechnic in Canada and a software automation intern at Communications & Power Industries (CPI), explains how he thinks entry-level jobs in tech have been impacted due to the emergence of AI: “The bar has been raised. For example, let’s say you are writing an API—Application Programming Interface. What you’d previously have to do was go through the documentation, read it, understand it, and then code. Now, it takes minutes to have a skeleton code. Because the workload is less tedious, you’re expected to know a lot more and do more as well.”

The development of generative AI has been meteoric. Most constraints that were conspicuous even a year ago have been rectified to various extents. Although new concerns have emerged, its integration into the professional realm has been evident. Even through AI’s limited capacity, it is able to execute tasks that are typically reserved for entry-level workers, which is to say repetitive and often mundane tasks that are low-stakes. Such a prospect is understandably anxiety-inducing for fresh graduates as well as for those on the precipice of graduation and even those who are currently pursuing their education.

Anica Hossain, a graduate student of communications at University of Liberal Arts (ULAB), a graphic designer, and owner of art merchandise business Anico, discusses the shift she has observed: “The development of AI has impacted the industry a bit. A lot of employers and brands tend not to hire graphic designers. They’re convinced that AI models can generate social media posts, write captions, and brainstorm ideas. But output that’s churned out by models distinctly looks like it was made by AI. Not everyone wants that kind of work. People still look for the human touch in the design. So, I don’t feel super threatened yet.”

Image: Google Deepmind

“Each artist and designer has their own distinct style, which I don’t think AI models can replicate yet. As an artist, I am unsettled that the work we create is used to train these models. In the future, these tools might create output that can emulate artists’ distinct styles. Right now, however, you still need designers to refine work generated by AI,” Hossain adds.

Having spent their formative years pursuing a qualification, only for it to be deemed inadequate, is discouraging. The purpose of entry-level jobs has been to help young graduates transition from the classroom to the professional realm. It is a period of training, one that isn’t just meant to teach and enhance the skills of fresh graduates but also allows them to figure out what kind of work they want to do. For such a crucial part of the process to become seemingly obsolete is disruptive in more than one way. In Bangladesh, particularly, this can amplify an already existing problem.


In a report by the World Bank, it was stated that unemployment in Bangladesh amongst tertiary-educated youth has increased significantly, with university-educated youth making up 27.8 percent of the total unemployment in 2022. An alarming rise from 9.7 percent in 2013. The rise in employment cannot be attributed to the rise of generative AI, though. However, with job creation stagnating and AI’s rapid improvement following in tandem, the state of the job market hangs in the balance.

Salma Begum, PhD, an associate professor at the Department of Economics and Social Sciences at BRAC University and a labour economist, analyses the dynamics of the current Bangladeshi workforce: “If we look at the research, it shows that the Bangladeshi labour market is at a low exposure level. Our exposure is well below the regional average when comparing neighbouring countries. This is because we depend more on agriculture and have informal services, which have not seen much automation so far. Digital inequality, in terms of access to digital literacy and relevant education, is another contributing factor.”

“AI models learn from data, and our country’s availability of good-quality, reliable data is low. This is a problem because while AI will eventually affect our market, our low exposure currently indicates our low preparedness for it,” she adds.

According to a report by the World Economic Forum (WEF), AI and information processing are projected to displace 9 million jobs, while creating 11 million new ones. It is expected that automation alone will cause 5 million jobs to be displaced. It must, however, be noted that the net disruption in the job market will be positive, with more jobs being created than displaced. What will be subject to change, though, is how people work, with the dynamic between humans and machines expected to alter.

“It is important to know and learn what AI tools can be leveraged to your benefit. This is something I do myself, and it does make working easier. I also think the ability to use AI tools might become a requirement in the future,” emphasises Hossain.

“Using AI in my workplace is encouraged. My employers have seen how powerful a tool it is. In fact, we have a proprietary AI model that employees are expected to use. In my workflow, I use it for debugging, and sometimes to find fixes as well. I will also use it to generate snippets of code that may be considered repetitive, low-risk, and not complicated. Lastly, I use it to write documentation whenever I am finishing up work on a big file, which is supposed to guide the next person who is going to work on it,” Ahmed affirmed.

Image: Immo Wegmann/ Unsplash

A point of contention still remains, of course. WEF asks that if AI and algorithms are contributing to a larger proportion of output and income, then where does that leave people? It, thus, becomes imperative that the significance of human-machine collaboration is reinforced when these inquiries are made.

Whether such considerations are going to be made remains to be seen. Based on findings from the World Bank, 1.2 billion young people in emerging economies will become working-age adults. In contrast, the job market is projected to only create 420 million jobs, leaving almost 800 million people in a precarious employment position. To tackle an issue of this magnitude, employers across the globe are emphasising the need to reskill and upskill.

“No technology is inherently good or bad. We can harness it for productivity and efficiency increases, which is how we utilise the benefits. But we can also not be prepared for it, for which we may pay a price in terms of lost employment and opportunities,” Salma Begum explains.

The conversation surrounding the impact of AI tends to devolve quickly into one of paranoia, where the most plausible outcome is envisioned to be the complete substitution of human labour. While these fears are by no means unfounded, what they do require us to do is take a step back, which can be difficult due to the uncertainty that has cloaked the entire situation. Nonetheless, the need to do so remains integral.

Though Ahmed finds the mechanism, even the scale and engineering, behind AI tools to be fascinating, he doesn’t think it is reliable enough to replace humans. “I have seen how AI models deal with complex tasks. Each one has a context window, which can be a limiting feature as it can forget vital information. You can’t replace humans with a tool that forgets or is unable to reason on its own. So, I just don’t see how software engineers and developers can be replaced, at least not with the technology we currently have.”

In a research paper by Isabeela Loaiza and Roberto Rigobon from the Massachusetts Institute of Technology (MIT) - Sloan School of Management, the authors introduced the EPOCH framework to understand how human capabilities have the capacity to complement, rather than compete with AI. The EPOCH framework identified five groups of human capabilities that allow work in areas where machines are limited. These include Empathy, Presence, Opinion, Creativity, and Hope.

Findings from the paper suggest that US workers are shifting towards tasks that “emphasise the human-intensive capabilities”. In fact, the results state that “new tasks” that appeared for the first time in 2024 are more human-intensive. It is also worth noting that human-intensive occupations were reported to experience more employment growth, hiring, and better projections. Some major occupations that had high EPOCH scores included Emergency Management Directors, Human Resource Managers, Sociologists, Clinical Psychologists, and Environmental Economists, amongst others.

Salma Begum reiterates that traditional career paths could take on a new form in the future, urging the young workforce to consider roles that bridge domains.

“Employers will want to hire people who are good learners, can solve problems, and work with new systems. Individuals who can navigate uncertainty and integrate new tools into their existing workflow are highly valued. Your ability to successfully adopt these systems in your workplace will matter more than your proficiency with any particular software,” she says.

When asked what students can do to adapt to the emergence of AI tools, Salma Begum emphasised the need to build a broad set of transferable skills along with proficiency in their core area, as boundaries between industries are being blurred.

There is little doubt that AI tools are a force of disruption, transforming and being integrated into workflows across sectors. What makes it a source of anxiety, however, is that we are still not sure just how disruptive it truly is. Given the rate at which it is progressing, the limits that previously defined the question are withering away; constantly expanding its range. While we don’t know the answer to this question, what it unravels is very human. As people, we are sceptical of the unknown. It is this instinct that forces us to ask questions, reason, and be critical. And this ability will determine not just how we understand AI tools but how we can use them to maximise their potential.

Abir Hossain is a sub-editor at Campus, Rising Stars, and Star Youth.​

 

[Saif]

https://thefinancialexpress.com.bd/views/views/ai-challenges-bangladeshs-promise-of-inclusion

AI challenges Bangladesh's promise of inclusion

Matiur Rahman

Published :
Feb 13, 2026 00:21
Updated :
Feb 13, 2026 00:21

Use of AI is rising in Bangladesh. A patient experiences a therapeutic equipment at the robotic rehabilitation center at Bangladesh Medical University (BMU), Dhaka, Bangladesh, Aug. 31, 2025 —Xinhua Photo

For more than a decade, 'Digital Bangladesh' was not merely a political slogan; it functioned as a practical framework for governance, service delivery, and socio-economic transformation. Internet connectivity expanded rapidly, government services became digitised, and millions of citizens-many of them poor and rural-were brought into the formal economy for the first time through mobile financial services. Technology, despite its limitations, carried a relatively inclusive promise. As Bangladesh now moves toward a new ambition often described as 'AI Bangladesh,' the stakes have become far higher. The central question is no longer about access alone, but about power, exclusion, and discrimination. Who will benefit from this transformation, and who will quietly fall behind?

Relatively low barriers to entry characterised the early phase of Digital Bangladesh. A basic smartphone and affordable mobile internet were often enough to participate in the digital economy. According to the Bangladesh Telecommunication Regulatory Commission, by October 2025, the country had around 132.5 million mobile internet subscribers. Mobile Financial Services accounts exceeded 125 million, with average daily transactions of roughly Tk 4,500 crore. These platforms opened unprecedented avenues of financial inclusion, particularly for the rural poor and low-income households. In the global freelancing market, Bangladesh emerged as the second largest supplier of online labour, with around 650,000 active workers. While these benefits were unevenly distributed, there were few institutional or technological barriers that actively excluded large segments of the population.

Artificial intelligence fundamentally alters this inclusive equation. AI is not simply an extension of digital connectivity. It relies on high-performance computing infrastructure, large volumes of structured data, advanced technical skills, and strong institutional capacity. The pathway to participation in 'AI Bangladesh' is therefore far narrower and far more expensive than that of Digital Bangladesh. This shift risks transforming technology from an enabler of inclusion into a driver of exclusion.

Data from DataReportal's 2025 annual report illustrates this concern clearly. While internet usage in Bangladesh has reached about 47.4 per cent of the population, only 16 per cent of households have access to high-speed broadband internet. In effect, more than half of the country's people remain on the margins of the digital world, relying on low-quality or unstable connections. If AI-enabled services in education, healthcare, finance, and public administration are built on top of this existing digital divide, they will inevitably favour a small, affluent, and urban segment of society. Those who are already ahead technologically will accelerate further, while those left behind may face a deepening and more permanent form of inequality.

The labour market is where the disruptive potential of AI becomes most visible. Around 84.9 per cent of Bangladesh's total employment is concentrated in the informal sector, where work is often repetitive, low-skilled, and vulnerable to automation. The International Labour Organisation estimates that roughly 54 per cent of occupations in Bangladesh will undergo some degree of transformation over the next decade due to AI and automation. This does not necessarily imply mass unemployment, but it does signal profound changes in job content, skill requirements, and employment security.

Globally, advanced economies are already boosting productivity through robotics and AI-driven software. Bangladesh is following this trajectory, albeit unevenly. AI applications are increasingly visible in manufacturing, transport management, retail operations, logistics, and even routine office work. While this may enhance efficiency and competitiveness, it also threatens millions of low-skilled workers. Occupations such as driving, data entry, and courier services are particularly exposed. Without viable pathways for reskilling and alternative employment, the country risks a surge in structural unemployment. Research by the Bangladesh Institute of Development Studies shows that around 33 per cent of educated unemployed individuals already lack the appropriate digital skills to secure jobs-a gap that is likely to widen in the AI era.

The readymade garment sector, the backbone of Bangladesh's export economy, provides a stark illustration of these dynamics. Large and modern factories have begun adopting automatic cutting machines, AI-based quality control systems, and smart production management tools. According to the Centre for Policy Dialogue, over the past decade, increased mechanisation in the garment sector has been associated with a decline of about 22 per cent in employment intensity for every additional unit of investment. While productivity and compliance standards improve, employment opportunities-especially for low-skilled workers-shrink.

This trend has serious gender implications. Nearly 60 per cent of the garment sector's approximately four million workers are women. Without a coherent national strategy for technical upskilling and workforce transition, AI risks creating a new hierarchy within the sector itself. Highly skilled technicians and engineers may see wage increases of 30 to 40 per cent, while ordinary sewing operators face job losses and wage stagnation. This is not merely an economic issue; it carries profound social consequences for female labour force participation, household welfare, and urban poverty.

The rural-urban divide further complicates the AI transition. While mobile internet coverage now reaches almost all parts of the country, broadband speed and stability remain weak in rural areas. According to the Bangladesh Bureau of Statistics ICT Usage Survey 2024, only 3.8 per cent of rural residents use a computer or laptop, compared to 19.5 per cent in urban areas. AI-powered solutions in education, healthcare, and agriculture may appear promising in policy documents, but in practice, they risk becoming urban-centric. A marginal farmer without access to AI-based weather forecasting, soil analysis, or market intelligence tools cannot compete with larger, technologically enabled agribusinesses. Over time, this digital divide could translate into widening income and opportunity gaps between rural and urban populations.

Education stands at the core of this challenge. AI-driven economies demand skills such as problem-solving, analytical reasoning, adaptability, and ethical judgment, rather than rote memorisation. The World Economic Forum estimates that AI-related technical competencies will account for around 44 per cent of required job skills by 2030. Yet Bangladesh's education system remains poorly aligned with these demands. While students in elite English-medium schools and expensive private universities increasingly gain exposure to coding, data science, and AI tools, around 75 per cent of students in government schools and madrasas lack access to adequate laboratories, trained teachers, or even reliable internet connectivity.

This educational divide risks producing a small 'AI elite' that dominates high-value jobs, corporate leadership, and policy-making, while the majority of young people are locked out of future opportunities. Talent alone will not be enough; without access and institutional support, capable students from ordinary families may be excluded from the AI-driven economy.

Gender inequality adds yet another layer of risk. In Bangladesh, women's internet usage and smartphone ownership rates are 29 per cent and 21 per cent lower than those of men, respectively. A GSMA report identifies Bangladesh as having one of the largest gender gaps in mobile internet usage in South Asia. At present, there is no comprehensive national initiative aimed specifically at increasing women's participation in advanced technology and AI research. Without targeted policies, AI may reproduce and even amplify existing gender disparities rather than serve as a tool of empowerment.

Older citizens are also being overlooked in this technological transformation. Senior citizens account for around 8 per cent of Bangladesh's population, yet many struggle with digital interfaces in banking, healthcare, and government services. As automation and algorithmic decision-making expand, there is a real risk that technological complexity could translate into the denial of basic rights. Efficiency gains must not come at the expense of accessibility, accountability, and human dignity.

AI's dependence on data introduces further concerns. Data collection in Bangladesh remains fragmented, poorly regulated, and heavily urban-biased. Marginalised groups are often underrepresented or absent from official datasets. When AI systems are used for decisions such as loan approvals, job screening, or welfare targeting, these data gaps can result in algorithmic bias-systematic discrimination embedded in mathematical models. Moreover, heavy reliance on foreign AI platforms raises questions of data sovereignty and national security. Without developing local AI models that reflect the Bangla language, culture, and social realities, Bangladesh risks long-term technological dependency.

Artificial intelligence undoubtedly offers enormous potential for Bangladesh, from disaster prediction and healthcare diagnostics to efficient governance and productivity growth. However, realising this potential requires a coordinated and inclusive strategy. Education reform must prioritise hands-on learning, coding, and AI ethics over rote memorisation. Infrastructure investment must focus on narrowing the urban-rural broadband gap in both speed and affordability. Large-scale reskilling and transition funds are essential for workers at risk of displacement. Strong data protection laws and ethical guidelines must safeguard citizens' rights. Above all, gender inclusion and support for marginalised groups must be embedded at the heart of AI policy.

The transition from Digital Bangladesh to AI Bangladesh is not simply a technological upgrade; it is a deeply social and political choice. If AI-driven growth enriches only a small, privileged class while rendering millions unemployed or irrelevant, it will lack any moral foundation. Development has meaning only when it improves the lives of those at the lowest rungs of society. AI must not become a glamorous slogan detached from social reality, but a tool for empowering every citizen. The greatest challenge facing Bangladesh is to ensure that no one becomes outcast or invisible in this wave of technological change. Only by placing human dignity and social justice at the centre of AI adoption can Bangladesh truly build a resilient and inclusive future.

Dr Matiur Rahman is a researcher and development professional.​

 

[Saif]

AI not magic wand for all our problems

In recent discussions in the business circles and among the policymakers, the AI-driven solutions have become the watchword. It is as though humanity has found the ultimate magic wand to resolve all problems ranging from healthcare to governance to economic development of a country. This infatuatio

thefinancialexpress.com.bd

AI not magic wand for all our problems

SYED FATTAHUL ALIM
Published :
Feb 22, 2026 23:19
Updated :
Feb 22, 2026 23:19

In recent discussions in the business circles and among the policymakers, the AI-driven solutions have become the watchword. It is as though humanity has found the ultimate magic wand to resolve all problems ranging from healthcare to governance to economic development of a country. This infatuation with Artificial Intelligence or AI, has actually started following the introduction of ChatGPT by an American artificial intelligence research and deployment company, OpenAI, from late 2022. Before that AI was considered a highly advanced area of computer science and was limited to research laboratories only. So, before 2022, it could not capture the imagination of the common people, like it is now. Especially, the fascination has centred around what is called Generative AI, the kind of AI that can produce different kinds of content including text, imagery, audio and synthetic data. It appears, AI is now the mythological Prometheus that has given the long-sought-after fire to spur civilization to a new height. The generative AI is based on Large Language Model (LLM) that can understand, summarise and predict human-like text. One often comes across such experience when Google comes up with a number of suggestions as soon as a text is written on it as part of an enquiry. That, in other words, is the ability of the AI to predict what text might the human enquirer would write next on the search engine (Google). In fact, it is the predictive characteristic of the Generative AI that has left us all believing that the new technology (AI) can do the job of thinking for us. So, to many, it appears that from now on AI would do the task of thinking for us. It is against such a background that we often come across ads on the mainstream as well as social media on training on AI that could turn the recipient (of the training) into a superhuman overnight. But things are not as simple. AI's predictive capacity relies on a massive amount of data supplied to it. Notably, the data are raw materials that make information after processing. To be sure, AI is not creative like us and cannot come up with new ideas to resolve our problems. The hard thinking has to be done by our businessmen and policymakers themselves to solve the problems of say, what course of action needs to be taken to recycle or destroy the usual household or toxic clinical wasters the cities produce every day. Turning to the area of governance, corruption is undoubtedly a big issue? Can AI run the anti-corruption body to identify sources of corruption and address the age-old problem in an unbiased manner? Notably, as discussed in the foregoing, the AI's strength lies on the fact that it can process untold amounts of data and extrapolate it to apply in the future.

Depending on the type of the data provided, the decisions or predictions made by AI can be highly biased or prejudiced. The reason is, the data AI learns from might themselves be biased along the line of colour, gender, faith or genetical issues of the population being served by a particular corporate or government agency. The biases emerge in the form of data, algorithm or human cognitive biases of the developers of AI systems during model training. For instance, an AI tool developed by Amazon had to be scrapped as it penalised hundreds of resumes containing the word 'women's'. It was found that the AI tool in question was trained on a hiring data that had been male-dominated for decades. Similarly, in the case of criminal justice, the AI tool, styled, 'COMPAS recidivism risk-assessment tool' disproportionately labelled defendants from the black community as 'high risk' compared to white defendants. In other cases, such as autonomous driving, the main source of AI's strength may prove to be its weakness. For its ability to predict the future creates what management experts term 'confidence trap'. Which is about the tendency to make assumptions like this: since earlier decisions have led to positive outcomes, continuing it the same way in the future would also continue to be correct. But just extrapolating the successful result of a past action and making decisions based on that without human intervention may lead to disastrous consequences. Let us consider the case of driverless of AI cars. As records go, the experience with 'driverless cars' has not been so comfortable as thought. There are reports of about a dozen or more pedestrians fatalities involving driverless cars so far. Though this figure hardly bears comparison with the number of fatalities caused due to human errors, still, the AI-related errors leading to fatalities would demand serious attention. For when it comes to decisions on a scale that involve the lives of a still larger number of human beings, say, in the case of airplanes carrying a large number of people and the same AI-based solutions in decision-making being applied to all the planes flying across the globe at a particular point of time, it sounds like a recipe for disaster. In the domain of healthcare, the issue is further concerning. In the USA, for instance, a widely-used algorithm to manage patient care was found to favour white patients over sicker black patients. It was again the biased algorithm that lay behind the colour-bias of the healthcare service under study. But does it mean that use of AI should be avoided in the case of healthcare or clinical medicine? The examples point to the fact that AI is not cure-all, neither is it infallible. Even so, AI would be a powerful aid to enable human healthcare professional in their decision-making. But one cannot leave the job to the 'AI-health expert' entirely. Human intervention is indispensable at every step, especially where critical decisions are to be made. It is so in the case of decisions on governance issues, development planning, planning budgets, collecting revenues, operating the complex traffic systems and so on.

At every step of the decision-making human intervention would be required to guide the AI-based operational tools. In sum, the users would be required to know the basic nature of the algorithm or the data-set that operates a particular type of AI-tool or system. In this connection, some experts in the area believe that the present madness over AI, especially in the West, characterised by heavy investments, excessively high valuation of startups, frantic startup building, etc., is leading to an AI bubble, which needs correction before it is too late.​

 

[Saif]

Where do workers stand in AI age?

The debate over artificial intelligence and the future of the job market has often swung between anxiety and excitement. Much of the excitement has come from the promise of a more efficient world where machines can produce code, analyse data and handle routine tasks with remarkable speed. At the sa

thefinancialexpress.com.bd

Where do workers stand in AI age?

MUHAMMED SHOWAIB
Published :
Feb 28, 2026 00:54
Updated :
Feb 28, 2026 00:54

The debate over artificial intelligence and the future of the job market has often swung between anxiety and excitement. Much of the excitement has come from the promise of a more efficient world where machines can produce code, analyse data and handle routine tasks with remarkable speed. At the same time, there was a growing fear that this very technology could bring about the biggest upheaval in work since the Industrial Revolution. Software engineers, lawyers, analysts and journalists have all heard the warning that machines may soon replace them. Some prominent researchers have even suggested that artificial intelligence could displace up to 80 per cent of software developers by 2025. That anxiety seemed to gain real weight when major technology companies began laying off hundreds of thousands of workers in 2024 and early 2025, making those predictions feel less like speculation and more like an approaching reality. And yet, now in 2026, the sweeping collapse of jobs that many expected has not materialised. Artificial intelligence has not wiped out work on a massive scale. Its impact has instead been uneven and, in many respects, more subtle. What does stand out, however, is that artificial intelligence is making it harder for younger workers to find a foothold in the job market.

The early promise of artificial intelligence helps explain why fears of widespread job losses took hold so quickly. For employers, the appeal was simply too hard to resist. If machines could perform the same tasks as humans, it seemed only natural to question why those roles should continue to be paid for. Companies were quick to see the potential for cutting costs, and before long, some of the largest firms began restructuring and laying off workers. Yet as experience has accumulated, those expectations have begun to look overstated. Tasks that demand judgment, contextual understanding and a sense of responsibility have turned out to be very difficult to automate. A recent report from the MIT NANDA Centre titled "The GenAI Divide" found that despite US$40 billion in global investment, as much as 95 percent of generative AI pilots in the enterprise sector have yet to produce a single dollar of measurable return. For most organisations, the impact on their bottom line has been zero.

One of the clearest disappointments of the artificial intelligence boom has happened in the very sector that gave birth to it. The belief that large language models could replace software developers has proved far less convincing in practice. Sure, artificial intelligence can generate simple programmes in seconds, but the outputs often become difficult to maintain over time. They lack the underlying structure needed for reliable systems, giving rise to what engineers describe as a slop layer. The term refers to code that appears to function at first but becomes difficult for human developers to understand and fix when problems arise. Analysis of 10 billion lines of code by CAS Software estimates that it would take 61 billion workdays to undo the damage caused by AI-assisted development. So in a strange way, artificial intelligence hasn't removed the need for skilled workers. If anything, it has changed the nature of their work. Instead of simply writing code, developers now spend more time reviewing, correcting and maintaining what machines produce.

This evolution is consistent with what we've seen in earlier waves of technological change. For example, the spread of computers didn't eliminate office work. Instead, it just automated the routine tasks which freed people up to focus on more complex and higher value activities. Now artificial intelligence seems to be following a similar path. It lowers the cost of certain mental tasks like drafting text or analysing data, but it doesn't wipe out entire jobs. Most jobs consist of a bundle of tasks, some of which can be automated and others that require human judgment. As long as this balance remains, full automation is going to stay the exception rather than becoming the rule.

That said, it would be misleading to conclude that artificial intelligence poses little threat to employment. The impact of the technology is not evenly distributed, and its most significant effects are hitting the entry level hardest. A lot of professions rely on junior workers to handle routine and repetitive tasks as part of learning the ropes and those are exactly the kinds of tasks AI happens to be really good at. As a result, employers suddenly have less reason to take a chance on inexperienced hires. In fact, because companies believed AI could handle junior-level work, entry-level hiring in the US dropped by nearly 50 per cent between 2023 and 2025. This is effectively cutting off the pipeline of future talent. When fewer juniors are brought in today, there are fewer experienced workers available in the years that follow. Over time, this weakens the entire system through which skills are developed and passed on.

Still, there are good reasons to think the long-term impact of artificial intelligence will end up being more balanced than the most pessimistic predictions suggest. Industries that have gone all in on AI are finally waking up to the fact that they are confronting a critical limitation. Artificial intelligence lacks accountability, a quality that underpins almost every form of human work. There have already been striking examples of what happens when that element is missing. In late 2025, the infamous "anti-gravity incident" occurred when a developer asked Google's anti-gravity AI to clear a project cache. The AI misread instruction and carried out a destructive command, wiping a two-terabyte production drive in seconds without permission. The AI's response was simply "I made a catastrophic error in judgment." But an apology doesn't exactly bring back months of lost work.

So where does this leave the office worker in 2026? AI has not taken over the workforce, and if anything, it has proven that knowledge work is rarely simple or easy to automate. The parts of a job that require genuine judgment, responsibility and oversight remain firmly in human hands. Still, for young people trying to enter a tight job market, the outlook is undeniably tough though maybe not completely hopeless.​

 

[Saif]

Bangla AI platform ‘Kagoj.ai’ launched

The first artificial intelligence technology-based platform for the Bangla language, ‘Kagoj.ai’, has been launched.

www.newagebd.net

Bangla AI platform ‘Kagoj.ai’ launched
Bangladesh Sangbad Sangstha . Dhaka 15 December, 2025, 22:17

Fayez Ahmad Taiyeb, special assistant to the chief adviser, inaugurate the first artificial intelligence technology-based platform Kagoj.ai on Monday. | BSS photo

The first artificial intelligence technology-based platform for the Bangla language, ‘Kagoj.ai’, has been launched.

Alongside it, a new Bangla font called ‘July’ has also been officially introduced.

The formal inauguration took place at Hotel InterContinental in Dhaka, at an event organised by the Information and Communication Technology Division on Monday, December 15.

Fayez Ahmad Taiyeb, special assistant to the chief adviser responsible for the Ministry of Posts, Telecommunications and Information Technology, inaugurated the platforms.

In his address, Taiyeb said at least 4,000 people have used the platform experimentally in the last two weeks and attained good results. Its API is being opened to make the Bangla language open to everyone, he added.Bangladesh country politics

He emphasised the importance of preserving linguistic and cultural diversity, announcing plans to collect 10,000 minutes of oral language data from each ethnic community. A Bangla Large Language Model (LLM) will be developed to help protect and sustain local languages in the digital space, he added.

He said the source code will also be opened by ensuring cyber security. To keep the local cultural flow of the language intact, 10,000 oral minutes of each ethnic group's language will soon be collected and Bangla LML will be created, he added.

He further noted that increased use of the platform would help identify challenges more quickly, accelerating development and improving the overall ecosystem.

It is mentionable that ‘Kagaz.AI’ will ensure the use of artificial intelligence in Bangla language writing, official document preparation, language processing and content creation, which is an important milestone in the digital advancement of the Bangla language.

Meanwhile, the new Bangla front ‘July’ has been created for official and institutional use, which will help remove various limitations existing in computer-based Bangla writing. The two services that were inaugurated have been developed through the ‘Enrichment of Bangla Language in Information Technology through Research and Development’ project implemented by the Bangladesh Computer Council under the Information and Communication Technology Division.

Secretary of the Information and Communication Technology Division Shish Haider Chowdhury presided over the event while Bangla Academy Director General Professor Dr Mohammad Azam, Additional Secretary of the Posts and Telecommunications Department Zahirul Islam, officials from various government and private departments, technology experts and invited guests were present.​

 

[Saif]

Will artificial intelligence widen inequality?

Artificial intelligence (AI) holds a lot of promise but also carries enormous risks.

www.thedailystar.net

Will artificial intelligence widen inequality?
Owais Parray

Artificial intelligence (AI) holds a lot of promise but also carries enormous risks. Aptly titled “The Great Divergence,” the UNDP Regional Human Development Report (RHDR) warns that, without decisive action, many developing countries in the region, owing to their weak capabilities, risk being left behind in the AI race. They will be unable to harness the upside of AI while mitigating the disruptions that have often accompanied frontier technologies. The risks are high as AI emerges amidst growing socioeconomic disparities among and within economies.

Like every major technological revolution—from steam power to electricity—should we expect a repeat of past trends from the emergence of AI, where inequality initially rises before benefits begin to diffuse? But we are entering the age of AI when inequality is already rising, exacerbated by the weakening relationship between GDP growth and job creation. For example, between 1995 and 2020, the top one percent captured 38 percent of global wealth, while the bottom 50 percent accounted for just two percent.

RHDR calls for embedding equity into policymaking. Equity cannot simply mean waiting for AI’s accrued benefits and redistributing them. It begins with AI that serves people and enhances human capabilities—a message that lies at the heart of human development. However, it cannot create equal opportunities for all if existing deficits in human, institutional, and financial capital are not recognised. Disparities in human development could be amplified by AI, a technology potentially as transformative as the steam engine and electricity. It is becoming a critical infrastructure that will determine the nature and pace of future development.

As the UNDP report shows, the Asia-Pacific will be a testing ground to see if AI-led development converges development outcomes or drives countries further apart. Countries in this region lie across a broad development spectrum: very large economies, small island nations, landlocked countries, and high-income economies (greater than $90,000 per capita GDP), alongside least developed countries (less than $500 per capita GDP).

From labour-intensive manufacturing, Bangladesh aspires to move into high-value production of goods and services—towards a more diversified, globally competitive economy, a country with a social safety net that supports not only those temporarily affected by the vagaries of the market but also those who are vulnerable and excluded. Moreover, a country with a public administration that is accountable and capable of delivering good quality services at scale. In all of this, AI could potentially play a critical role.

A forward-looking AI agenda for Bangladesh should focus on aligning it with the needs of people. Without a purpose-driven focus, there is a danger that AI won’t deliver its promise to improve human welfare. A good starting point for public policy is narrowing the digital gap. While connectivity has expanded in Bangladesh over the years, gaps still persist across rural areas, income groups, and gender. Women in Bangladesh are less likely to own a smartphone. Only 38 percent of women use mobile internet compared to 52 percent of men, severely limiting their ability to benefit from AI.

There may not be consensus on whether the net benefits of AI will outweigh its disruptions in the labour market, but many agree that countries must rethink education and training systems. Previous waves—such as industrial automation in the 1970s, ICT diffusion in the 1990s, or the introduction of robotics in manufacturing—initially caused job losses but eventually contributed to productivity booms. We cannot necessarily take past evidence and extrapolate it to the future. However, a key lesson is that education and training systems should be adaptive to rapid technological change, embracing lifelong learning. It serves as an insurance policy against joblessness as the labour market adjusts to technological change.

Furthermore, AI in public services should be underpinned by transparency. AI-assisted services should be clearly explained, open to scrutiny, and corrected when errors or biases occur. For example, Canada provides a set of guidelines for the use of AI and automated decision-making. Public trust must not be undermined. Without safeguards, biased algorithms can erode trust and cause lasting harm.

Bangladesh is in a region that is likely to drive future global growth. According to recent estimates, the Asia-Pacific region will contribute 60 percent of the global GDP growth. Asia is also emerging as an important hub of AI investment and AI patents. A large country with a young population, Bangladesh has a stake in championing responsible AI for prosperity, while strongly advocating for minimum standards and collective safeguards essential for human-centric AI development. Bangladesh has already conducted an AI Readiness Assessment that provides a solid foundation for developing an AI roadmap for the future.

Owais Parray is country economic adviser at United Nations Development Programme (UNDP), Bangladesh.​

 

[Saif]

AI adoption must put people first

ARTIFICIAL intelligence has reached a turning point. It is no longer confined to innovation labs or future-facing strategy documents. Increasingly, it shapes how organisations deliver services, manage risk and improve efficiency. For Bangladesh, this moment brings both promise and pressure, particularly in sectors such as telecommunications infrastructure that support national development. The opportunity is significant, but so is the responsibility to ensure that AI adoption strengthens human capability, supports inclusion and prepares the workforce for the next decade.Machine Learning & Artificial Intelligence

Connectivity has become a form of national infrastructure, underpinning education, commerce and participation in the digital economy. According to the Bangladesh Telecommunication Regulatory Commission, by November 2025 the country had 187.06 million mobile subscribers, supported by 46,685 towers across operators and licensed tower-sharing providers. Behind these numbers lie millions of livelihoods: farmers accessing weather forecasts, micro-entrepreneurs receiving digital payments, families sending remittances through mobile platforms, and students attending online classes.

Yet the digital landscape remains uneven. By late 2025 Bangladesh had about 82.8 million internet users, roughly 47 per cent of the population, meaning a majority still remained offline. Technologies such as AI do not arrive neutrally. They tend to amplify the systems they enter. Without deliberate investment in human capability and inclusive strategies, AI-driven progress could widen existing divides as easily as it accelerates growth.Bangladesh travel guide

For the telecommunications infrastructure sector, the case for AI is practical rather than fashionable. Tower operations involve thousands of distributed assets, strict uptime requirements and energy-intensive equipment. Globally, the energy footprint of digital connectivity is substantial. The International Energy Agency estimates that data transmission networks account for around 1 to 1.5 per cent of global electricity consumption. Improving efficiency is therefore both a business necessity and a climate priority.

Encouragingly, progress is possible. The GSMA reports that operational emissions across the mobile industry fell by 8 per cent between 2019 and 2023, even as connections expanded and data traffic grew rapidly. Applied intelligently, AI can help extend this trajectory through predictive maintenance, more efficient energy use and improved operational planning.

Yet technology alone does not determine the future of work. The decisive factor will be how people use these tools. The World Economic Forum estimates that between 2025 and 2030 structural transformation will affect 22 per cent of today’s jobs globally, with 170 million roles created and 92 million displaced. At the same time, nearly 39 per cent of workers’ existing skills may become outdated. If the global workforce were represented by 100 people, about 59 would require training by 2030, while 11 might not receive it, putting their livelihoods at risk.

The International Labour Organization adds an important nuance to this debate. Its recent assessment suggests that one in four workers are in occupations with some degree of exposure to generative AI, but the more likely outcome is job transformation rather than outright replacement. This distinction matters. If AI is treated merely as a software rollout, organisations risk missing the larger challenge. The real implementation is human.

In sectors such as telecom infrastructure, this people-centred approach is already beginning to take shape. Digitalisation efforts across the ecosystem increasingly combine technological adoption with workforce capability building, governance frameworks and strong human oversight. Instead of treating transformation as a standalone IT initiative, organisations are embedding digital tools directly into everyday operational processes.

In practice, this means using data-driven systems and automated workflows to improve visibility across tower performance, from crisis response and preventive maintenance to energy management and site operations. Many operators now rely on business intelligence dashboards, mobile workflow applications and robotic process automation to manage repetitive tasks more quickly and accurately.

The benefits go beyond efficiency. When routine processes become easier to manage, teams can concentrate on higher-value work such as coordination, troubleshooting and decision-making, areas where human judgement remains indispensable. Digitalisation can also strengthen transparency and operational trust. Centralised digital asset catalogues, for example, help improve data quality, streamline invoicing processes and reduce disputes between operators and partners.

At the same time, the next stage of transformation is taking shape through modernised operations centres capable of monitoring nationwide infrastructure in real time. Such systems can help optimise connectivity, manage energy use and improve service reliability. Even here, however, the technology functions best when paired with informed human oversight. Data provides signals, but people interpret those signals, make decisions and respond to unexpected challenges.

AI’s relevance extends beyond operational efficiency. In energy-intensive sectors such as telecommunications infrastructure, smarter operations increasingly intersect with sustainability goals. AI-enabled energy optimisation can reduce waste, lower emissions and improve resilience — outcomes that are particularly important for countries like Bangladesh, which remain highly vulnerable to climate risks.Machine Learning & Artificial Intelligence

Some companies operating in Bangladesh have begun exploring hybrid and renewable energy solutions at tower sites in order to reduce reliance on fossil fuels. Data-driven management systems can complement such initiatives by improving reliability while supporting long-term environmental sustainability.

Ultimately, the strongest argument for a people-centred approach to AI lies in the risks of inaction. Skills shortages are already identified globally as one of the main barriers to digital transformation. In Bangladesh, where millions remain outside the digital economy, an unmanaged transition could deepen inequality by creating a highly productive AI-enabled segment of the workforce alongside a larger group left behind.

For a country aspiring to build a future digital economy, that outcome would undermine both social inclusion and economic competitiveness. Addressing this challenge requires coordinated action. Businesses must treat workforce training as a strategic investment rather than an optional initiative. Policymakers must encourage responsible innovation through clear regulatory frameworks for data protection, accountability and digital infrastructure. Educational institutions, meanwhile, will need to adapt curricula to emphasise analytical thinking, ethical judgement and lifelong learning.

Artificial intelligence is no longer optional. Yet replacing people is not the objective. The real competitive advantage, particularly in critical infrastructure sectors, will belong to organisations that integrate technological capability with human expertise responsibly and inclusively. In the coming decade, success will depend not on how quickly systems can be automated, but on how effectively societies can build an AI-literate, adaptable and skilled workforce capable of using these tools wisely.​

 

[Saif]

AI-based legal platform 'FindmyAdvocate' launched in Bangladesh

An artificial intelligence (AI)-powered digital legal platform, ‘FindmyAdvocate’, has been launched in Bangladesh to simplify the process of obtaining appropriate legal advice and assistance from lawyers according to the nature of a problem.

en.prothomalo.com

AI-based legal platform 'FindmyAdvocate' launched in Bangladesh

Rahitul Islam
Dhaka
Published: 06 Mar 2026, 18: 06

An artificial intelligence (AI)-powered digital legal platform, ‘FindmyAdvocate’, has been launched in Bangladesh to simplify the process of obtaining appropriate legal advice and assistance from lawyers according to the nature of a problem.

With AI technology integrated into the new platform, users can quickly connect with specialist lawyers from anywhere in the country while sitting at home.

The platform essentially functions as a digital bridge between people seeking justice and lawyers. Its key feature is an AI-powered searching and chatting system that analyses the nature of a user’s legal issue and automatically suggests suitable lawyers.

In addition, users can receive preliminary legal guidance through the platform’s AI legal assistant, which remains active around the clock.


The FindmyAdvocate platform currently contains a database of registered lawyers providing legal services in all districts of the country. As a result, users can easily connect with lawyers for assistance in civil and criminal matters, tax and VAT issues, deed writing, marriage and divorce registration, as well as cybercrime-related legal services.

Because the platform includes information on lawyers’ experience, ratings and location, users can conveniently choose the most suitable legal professional.

Md Haider Tanviruzzaman, a lawyer at the Bangladesh Supreme Court and founder of the FindmyAdvocate platform told Prothom Alo, "In our country, obtaining advice from legal experts when it is needed is often complicated and time-consuming."

"One usually has to visit a lawyer’s chamber several times before securing an appointment, making the entire process lengthy and cumbersome. This platform has been launched to free people from that prolonged and harassing process," he aded.

The platform uses ‘CyTex AI’ technology developed by Bangladeshi innovator Ihan Chowdhury. In addition to finding lawyers, users can arrange legal consultations through their mobile phones, including video calls, chamber appointments or meetings with lawyers at home.

The platform also creates new opportunities for lawyers. Both new and experienced practitioners can create profiles, manage their practice online and organise their schedules through the system.​

 

[Saif]

https://en.ittefaq.com.bd/15753/ai-is-here%E2%80%94are-we-ready

AI Is Here—Are We Ready?

In the long history of human civilization, there have been certain inventions that completely transformed the way people live. The discovery of fire illuminated the darkness and laid the primitive foundation for cooking, protection, and technology.

The invention of the wheel greatly increased the speed of human movement and trade, planting the first seeds of globalization. The Industrial Revolution opened new horizons of production by harnessing mechanical power. In this historical progression, another groundbreaking force has clearly emerged in the present age—artificial intelligence.

Renowned technologists, economists, and policymakers around the world are already saying in unison that human civilization now stands at a critical crossroads. On one side lies the era when human memory, experience, labor, and judgment were the primary driving forces of civilization.

On the other side, a new age is beginning—one in which machines are not merely assistants to humans but are increasingly becoming active participants in decision-making processes. A future historian may perhaps divide the history of human civilization into two parts: the age of human intellect alone, and the age of intelligence created by humans.

The potential of this new technology is already beginning to manifest in many dimensions. In medical science, artificial intelligence is now capable of analyzing certain diseases with a precision that can surpass the human eye. In advanced hospitals, machines assist physicians by analyzing complex maps of a patient’s anatomy before surgery.

In agriculture, intelligent technologies analyze soil quality, moisture, weather changes, and production possibilities to help farmers make accurate decisions. In the judicial system, artificial intelligence can quickly organize arguments and information by analyzing millions of past legal precedents. In the world of economics, algorithmic technologies are also becoming increasingly effective in analyzing market uncertainty, risks, and trends.

The current of change is flowing rapidly in the field of education as well. Modern educational theory states that every student’s intellectual inclination and pace of learning are different. Artificial intelligence can analyze these individual variations and design separate learning pathways for each student.

As a result, the schools of the future may no longer rely on a single textbook; instead, a new model of technology-driven, personalized education may emerge.

This reality draws our attention to a profound truth: human civilization is gradually moving from a “machine-assisted” era to a “machine-directed” era. Forward-thinking nations have already begun placing artificial intelligence at the center of their national education policies.

Singapore has gradually integrated technology and AI education from primary to higher education. South Korea has taken initiatives to build foundations in coding, data analysis, and machine learning at the school level. Many European countries have introduced open AI curricula to make citizens technologically aware.

The United Arab Emirates has gone even further by planning to strategically integrate artificial intelligence into governance and administrative structures. There are even rumors that artificial intelligence is being used in conflicts such as the Iran–United States–Israel tensions.

It is clear that those who embrace this new chapter of technology today will lead the knowledge-based world of tomorrow. In this context, it is essential that we turn our attention to ourselves. Are we sufficiently prepared for this transformation? Is our education system being built with the technology-driven world of the future in mind?

If not, then far-reaching planning is needed immediately. At the primary level, technological literacy should be introduced; at the secondary level, concepts of data thinking and algorithmic reasoning; and at the higher-education level, subjects such as machine learning, robotics, and cybersecurity should be incorporated.

At the same time, it is equally essential to prepare the teaching community with modern technological knowledge.

A new chapter in the history of human civilization has already begun. The question now is no longer whether artificial intelligence will transform our society. The real question is this: will we write the history of this transformation ourselves, or will we remain content merely reading a history written by others?​

 

[Saif]

Bangladesh must adapt as AI transforms diplomacy and state power

Artificial intelligence (AI) is no longer a distant technological frontier. It is already shaping how governments make decisions, information circulates, borders are managed, and how citizens interact with the state.

www.thedailystar.net

Bangladesh must adapt as AI transforms diplomacy and state power
8 April 2026, 09:00 AM

Mohammad Khorshed A. Khastagir

VISUAL: ANWAR SOHEL

Artificial intelligence (AI) is no longer a distant technological frontier. It is already shaping how governments make decisions, information circulates, borders are managed, and how citizens interact with the state. For Bangladesh, this transformation is increasingly visible in everyday realities—from visa applications to labour markets, from digital services to global narratives about who we are. Despite that, the national conversation on AI remains largely confined to innovation, startups, and automation.

What is less discussed, but far more consequential, is that AI is becoming a new domain of diplomacy and sovereignty. If Bangladesh does not recognise and respond to this shift, it risks entering a future where decisions affecting its citizens are increasingly made or mediated by systems it neither designs, governs, nor adequately controls.

For decades, diplomacy has operated within a familiar framework. States negotiated with states, sovereignty was territorial, and rules were defined through treaties such as the Vienna Convention on Diplomatic Relations. Today, that landscape is changing in a transformative way. A small number of global technology actors—companies controlling cloud infrastructure, data systems, and AI platforms—now influence how information flows, how risks are assessed, and how decisions are made. These systems underpin everything from financial transactions to migration screening. In practical terms, power is increasingly exercised not only through institutions but through algorithms and digital infrastructures.

For countries like Bangladesh, this creates a new challenge. Engagement is no longer limited to governments. There is also a growing, often indirect, interaction with systems designed elsewhere, trained on foreign data, and aligned with external institutional assumptions. This asymmetry is not merely technical; it is structural, with implications for how sovereignty is exercised in a digital age.

A clear example of this transformation can be seen in visa processing. Across many developed countries, AI-powered tools are now used to assess visa applications. These systems analyse risk patterns, flag anomalies, and support decision-making processes. While presented as efficient and objective, they often operate through opaque models trained on historical data. For Bangladeshi applicants, this can translate into a difficult reality: decisions that are increasingly automated, less transparent, and harder to challenge. When a rejection is issued, it is often unclear whether it reflects a human judgement or an algorithmic risk profile shaped by past data biases.

Beyond formal documentation, there is also a growing perception that applicants’ digital footprints, including social media activity such as Facebook profiles, may be informally scrutinised to assess attitudes, affiliations, or perceived behavioural risks. Whether systematic or discretionary, such practices introduce an additional layer of opacity, where personal expression in digital spaces may indirectly influence mobility outcomes.

Compounding this challenge are emerging concerns regarding identity credibility. Perceptions, particularly in some destination countries, that individuals from forcibly displaced Rohingya populations may have obtained Bangladeshi passports through systemic leakages, have begun to cast a shadow over the integrity of Bangladesh’s otherwise advanced e-passport system. Even if limited in scale, such narratives can disproportionately affect trust in documentation, reinforcing risk perceptions within algorithmic systems that rely heavily on identity assurance.

This evolving reality is not merely a consular issue. It reflects a broader shift in how borders are governed—through what may be described as algorithmic border regimes. If states do not understand how such systems operate, they cannot effectively engage, negotiate, or contest outcomes. In this sense, visa challenges are an early indicator of a deeper transformation in global governance.

At the same time, another structural shift is underway. AI is no longer confined to industrial automation; it is increasingly capable of performing white-collar tasks such as drafting documents, analysing data, translating content, and supporting legal or financial decisions. This raises important questions for Bangladesh’s economic model, particularly in relation to overseas employment and remittance flows. If global demand for certain categories of labour shifts due to AI-driven efficiencies, the implications will extend beyond economics into diplomacy, affecting labour agreements, migration pathways, and the protection of workers abroad.

An often overlooked dimension of this transformation is language. Large AI systems do not simply process languages; they reason through them. When a language is underrepresented, the system struggles to interpret its legal concepts, institutional frameworks, and social nuances. Bangla, despite being spoken by nearly 300 million people, remains underrepresented in many global AI architectures. This creates a subtle but significant distortion: national realities are filtered through external linguistic and conceptual frameworks. In this context, language becomes not just a cultural asset but an infrastructure of sovereignty.

These developments point to a necessary conclusion: diplomacy must evolve. AI can no longer be treated as a purely technical domain managed by ICT agencies. It must be recognised as a strategic field requiring diplomatic engagement, policy coordination, and institutional capacity. This includes understanding how AI systems influence migration and visa decisions, engaging in global AI governance discussions, ensuring representation of Bangla in AI ecosystems, and preparing for labour market transformations driven by technological change.

One practical step forward would be to strengthen institutional capacity within the foreign policy apparatus. This could involve creating a dedicated focus on AI and digital sovereignty within existing structures, enabling more systematic monitoring of global developments, more effective international engagement, and more informed policy responses. This approach aligns with emerging efforts to modernise diplomatic frameworks for the digital era, including proposals to extend traditional diplomatic protections into cyberspace and AI-mediated environments.

The urgency of this shift cannot be overstated. Global investments in AI infrastructure are accelerating, governance frameworks are being shaped in international forums, and standards are being set, often without meaningful participation from developing countries. If Bangladesh remains passive, it will have to adapt to systems designed by others. If it acts strategically, it can help shape those systems, ensuring that they reflect its realities, its language, and its national interests.

Evidently, the future of sovereignty will not be determined solely by borders, treaties, or military capabilities. It will increasingly depend on who understands, shapes, and governs the digital systems through which power is exercised. Artificial intelligence is not replacing diplomacy; it is redefining it. For Bangladesh, recognising this shift is the first step. Acting on it is the real test.

Mohammad Khorshed A. Khastagir is a diplomat currently pursuing a PhD at Taylor’s University, Malaysia. He is also a visiting researcher at the Centre for Industrial Sustainability, Institute for Manufacturing, University of Cambridge.​

 

[Saif]

How AI could curb mobile banking fraud

The mobile banking landscape in Bangladesh has experienced phenomenal growth over the last decade

www.thedailystar.net

How AI could curb mobile banking fraud

12 April 2026, 17:00 PM
UPDATED 12 April 2026, 17:00 PM

Shuvashish Roy and Md Tuhin Rana

As mobile financial fraud grows increasingly sophisticated, a new intelligent system tracking how users type and swipe offers a powerful shield for honest customers.

Imagine a university student in Dhaka preparing to pay her final semester tuition fees, or a small business owner in a busy market counting their monthly earnings. Suddenly, they receive an urgent phone call from someone claiming to be a customer support executive from their trusted mobile banking provider. The caller sounds incredibly professional, warning them that their accounts will be permanently blocked due to a sudden system upgrade unless they verify their details immediately. Panicked and rushed, they follow the instructions without a second thought. Within minutes of a seemingly harmless interaction, the student's entire tuition and the business owner's hard-earned profits are completely drained. These scenarios are no longer isolated nightmares, but a reality for many across the country. Fraudsters have evolved far beyond simple trickery, deploying advanced social engineering tactics that bypass standard security measures and leave regular citizens financially devastated before they even realise what went wrong.

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The mobile banking landscape in Bangladesh has experienced phenomenal growth over the last decade. Platforms such as bKash, Nagad, and Rocket have fundamentally transformed the concept of financial inclusion, bringing over 144 million registered users as of January 2026 into the formal economy as per Bangladesh Bank data, among which 5.70 lakh are relatively more vulnerable youth accounts.

Driven by rising internet penetration and the widespread availability of smartphones, everyday transactions have migrated to digital screens. However, this massive shift has simultaneously attracted highly organised fraud syndicates. As reported by The Daily Star in May 2024, a total of 48,586 personal mobile financial service accounts have been suspended by the Bangladesh Financial Intelligence Unit (BFIU) for suspected involvement in online gambling, betting, and hundi.

Fraudsters are siphoning millions of taka from unsuspecting users through fake investment schemes, cloned emergency numbers, and highly coordinated social engineering tactics. As transaction volumes surge to thousands of crores daily, the financial and emotional toll on everyday users is mounting. This growing epidemic directly threatens the core trust required for a thriving digital economy, making it a critical national issue that demands immediate intervention.

The primary vulnerability enabling these crimes lies in how current security systems operate. Traditional banking defences rely heavily on rigid, rule-based methods that simply monitor for obvious red flags, such as multiple incorrect PIN entries or exceptionally large, uncharacteristic transfers. Unfortunately, today's sophisticated fraudsters rarely hack into systems using brute force.

Instead, they manipulate victims into willingly sharing One-Time Passwords, or they use stolen credentials to log into the application in a completely standard manner. Because these criminals meticulously mimic legitimate login procedures, traditional security rules simply cannot distinguish between the actual account owner and a thief in a remote location. If the PIN matches and the One-Time Password is correct, the system blindly assumes the transaction is safe and processes the theft.

To combat this rapidly evolving threat, our research introduces a much smarter, highly adaptive framework. We focused on the emerging concept of behavioural biometrics, which operates on a simple but powerful principle: how you type, swipe, and scroll on your phone screen is as unique to you as a physical fingerprint. When this continuous behavioural data is combined with transactional patterns, such as where you are located, when you normally send money, and how much you typically transact, a highly comprehensive behavioural profile emerges.

Developing this solution involved a deliberate progression of machine learning models. We initially utilised an autoencoder to strictly profile normal user behaviour. We then moved to advanced networks capable of capturing time-based sequences, applied gradient boosting techniques, and finally combined these elements into a robust ensemble system capable of learning from vast amounts of data.

The performance results of this hybrid research work are highly encouraging for the future of mobile security. Our system achieved an impressive 97 percent fraud detection rate alongside 95 percent precision. To put this improvement into perspective, the initial baseline model operating alone missed 67 percent of fraudulent activities. This massive leap in accuracy means the system is not just catching more criminals, but doing so with remarkable exactness. The high precision rate offers a crucial practical benefit: fewer false alarms. This ensures that honest customers do not face frustrating delays or unexpectedly blocked accounts while attempting to make legitimate payments. In analysing the framework, we discovered that the most critical indicators for spotting an anomaly are the user's geographic location, combined directly with their unique scrolling and typing speeds.

For Bangladesh, adopting this kind of intelligent framework could be entirely transformative. Regulatory bodies like Bangladesh Bank, alongside leading mobile financial service providers, have an immediate opportunity to integrate these predictive models directly into their existing digital infrastructures. Because the framework is designed to be highly adaptive and locally relevant, it offers a real-time, deployable defence against the specific social engineering tactics currently prevalent in our ecosystem. Such proactive security mechanisms are absolutely essential for securing the next phase of the nation's journey toward a truly cashless society.

Securing our digital economy requires a rapid shift from reactive troubleshooting to proactive, artificial intelligence-driven defence. It is imperative for regulators, traditional banks, and fintech companies to collaboratively invest in advanced behavioural protection. By embracing these intelligent systems today, the financial sector can finally outpace the fraudsters and ensure that digital financial services remain a safe, empowering tool for every citizen.

Shuvashish Roy is a senior researcher at the Research & Innovation Division of Prime Bank PLC, and Md Tuhin Rana is a student of the Department of Statistics at the University of Dhaka​

 

[Saif]

The Financial Express | First Financial Daily of Bangladesh

Get latest stock share market news, financial news, economy news, politics news, breaking news, Bangladesh economy news at The Financial Express.

thefinancialexpress.com.bd

AI-powered embedded finance: ethics at a crossroads

Sanjoy Pal

Published :
Apr 24, 2026 23:58
Updated :
Apr 24, 2026 23:58

Artificial Intelligence (AI), a significant technological advancement, is currently influencing activities across the globe, from less developed rural areas to highly industrialised economies. The applications of AI are not confined to daily human life but extend to crucial sectors such as medicine, innovation, research, education, and economic growth. Automation possesses the remarkable capacity to unify the global community under a singular framework. Financial sector around the world is experiencing more service propositions under Artificial Intelligence framework. The developed economies are actively pursuing enhancements in AI integration. In contrast, the developing economies are adopting AI solutions presenting a dual-edged promise, by expanding financial inclusion for unbanked populations while also risking the amplification of inherent systemic biases. In the regions like Bangladesh, where 68 per cent of banks have a gap in operational AI policies, the ethical considerations surrounding AI is currently transitioning from initial exploratory phases towards formalised regulatory frameworks.

The global financial services landscape is being remodeled by Artificial intelligence and embedded finance. From instant loan at checkout to invisible payments by online banking application and mobile platforms, AI-powered embedded finance has profoundly reshaped user experiences. Consequently, the AI integration into embedded finance model facilitates accelerated, predictive and self-governing financial decision-making. As AI adoption in finance escalates, the industry is evolving towards "Embedded Finance 2.0" integrating "Agentic AI," characterised by autonomous systems assuming responsibility for real-time financial decisions in lieu of human intermediaries. However, a fundamental query emerges: will this technological advancement outpace ethics without consequences?

COMPREHENDING THE TRANSFORMATION: Embedded finance denotes the incorporation of financial functionalities including payments, lending, and insurance into non-financial platforms such as online marketplaces, ride-sharing services, or social media networks. Artificial Intelligence amplifies this model by facilitating instantaneous credit scoring, sophisticated fraud detection, and tailored financial advices through transforming the Embedded Finance model from '1.0' to '2.0'. The profound transformation initiates a seamless and almost invisible financial ecosystem, where decisions are executed with extreme rapidity. However, this invisibility also introduces ethical opacity.

THE BLACK BOX DILEMMA: The phenomenon of the AI's "black box" nature, as elucidated by IBM, stems from the inaccessibility of its intricate internal mechanisms, which encompass millions of parameters and complex, non-linear computations. Comprehending the decision-making processes of artificial intelligence presents a significant challenge. The underlying algorithms responsible for approving or modifying loan pricing within embedded finance platforms often lack explicit explanation and transparency. This deficiency erodes customer confidence and engenders questions regarding accountability. Determining responsibility-whether it lies with the platform, the financial institution it represents, or the algorithm itself-becomes problematic.

BIAS IN AI SYSTEMS: Historical financial data frequently exhibits ingrained inequalities, which can cause AI-driven embedded finance to inadvertently perpetuate discrimination, especially impacting low-income or marginalised communities. This danger is particularly significant in developing regions like Bangladesh and other parts of South Asia, where financial inclusion is a key objective. Inadequately constructed AI systems, rather than facilitating greater access, risk exacerbating disparities by systematically denying vulnerable segments of the population opportunities for credit or insurance.

DATA CONFIDENTIALITY AND AUTHORISATION: Embedded finance is heavily reliant on data-extensive personal, behavioural, and transactional information. AI algorithm process this data to predict user requirements and automate decisions. However, this dependency and trust concerns seriously about privacy and informed consent.

Consumers generally do not know how their data are being managed in AI platforms. In many cases, consent becomes a formality rather than a meaningful choice. In the absence of data governance frameworks, data breaches or misuse of sensitive financial information can deteriorate consumer confidence and expose institutions to legal risks.

AUTONOMY VS. MANIPULATION: In the embedded finance autonomous ecosystem, AI serves as a primary driver engineered for proactive, even predictive. It can score the customers analysing real time transactional data, suggest contextual credit offers, trigger payment routing, or recommend investments to users. While this promotes user convenience, it also shadows the distinction between supportive guidance and undue influence.

Behavioural prompts, if not ethically considered, possess the potential for exploiting cognitive biases. For instance, offering immediate credit at the point of transaction could foster spontaneous borrowing rather than prudent financial conduct. Ethically sound embedded finance necessitates that such nudges empower users rather than take advantages of them.

SYSTEMIC RISKS AND MARKET RESILIENCE: The extensive integration of AI in financial sector hosts significant systemic risks. When numerous platforms rely on similar algorithms and datasets, their decisions thereby foster "herding" tendencies in financial markets.

In the embedded finance ecosystem, where transactions are in interconnected landscape across platforms, such synchronised actions possess market volatility leading to new forms of financial instability. The risk is not just technological-it is structural.

THE GOVERNANCE CHALLENGES: Embedded finance ecosystem inherits a diverse and complex governance including the participants like fintech platforms, banks, regulators, and technology providers. This intricate network complicates accountability and regulatory oversight.

Traditional regulatory structures were not originally designed to accommodate such deeply integrated financial systems. Leaving critical issues concerning responsibility, liability, compliance, and consumer protection remain unresolved. The absence of well-defined governance mechanisms creates vulnerabilities wherein ethical transgressions may go unaddressed.

TOWARDS THE ETHICAL BEST PRACTICES: Achieving ethical standards in embedded artificial intelligence within the financial sector necessitates a comprehensive strategy. Hence, addressing the challenges stated above requires a multi-dimensional approach, including

n Transparency and Explainability: Decision-making processes should be transparent and comprehensible to users through implementation of "Explainable AI" (XAI).

n Fairness, Inclusion and Auditing: AI models should be regularly audited to mitigate biasness and promote fairnesswith regulations such as the EU AI Act.

n Data Protection: Robust safeguard measures must be ensured to safeguard privacy and obtain informed consent.

n Human Oversight: Mandating human oversight must be maintained for critical decision-making processes, rather than relying solely on automation.

n AI Ethics Committees: Cross-functional boards (legal, data science, ethics) to review AI projects for potential harms must be established within the financial institution.

n Emerging Regulatory Landscape: Regulators and policymakers must evolve frameworks to keep pace with technological advancements.

Ethical AI transcends mere regulatory compliance; it serves as the foundation for sustainable innovation. Financial institutions that consider ethics in AI as premium will not only avert adverse regulatory actions but also foster long-lasting stakeholder relationships. In a strategic maneuver by the Bangladeshi government, the "National AI Policy, Bangladesh: 2026-2030" is presently undergoing review and awaits final approval, aiming to position the nation prominently within the artificial intelligence domain among South Asian nations.

The future of embedded finance, moving toward agentic AI, relies on maintaining public trust. The evolution promises to enhance financial accessibility, improve operational efficiency, and redefine user experiences. Nevertheless, in the absence of a robust ethical framework, it carries the potential to become a mechanism for disenfranchisement, undue influence, and systemic instability. The unchecked advancement of AI in this domain risks perpetuating historical disparities into "efficient injustices," whereas proper implementation offers unprecedented opportunities for financial inclusion. Hence, the ultimate future of finance will be determined not merely by how seamlessly it integrates into our lives, but how responsibly it contributes to our well-being.

Sanjoy Pal is a researcher, banker and Visiting Faculty of an institute affiliated with National University, Bangladesh.​

 

[Saif]

Creating sound energy base to meet AI dream

Evidently, the quality of internet service and connectivity in Bangladesh is one of the poorest in the world. But this is not an attempt at finding fault with the government without rhyme or reason. The prime minister's adviser on telecom and ICT, Rehan Asad, himself expressed such views about our

thefinancialexpress.com.bd

Creating sound energy base to meet AI dream

SYED FATTAHUL ALIM

Published :
Apr 27, 2026 00:11
Updated :
Apr 27, 2026 00:11

Evidently, the quality of internet service and connectivity in Bangladesh is one of the poorest in the world. But this is not an attempt at finding fault with the government without rhyme or reason. The prime minister's adviser on telecom and ICT, Rehan Asad, himself expressed such views about our internet service while speaking the other day at a seminar styled, 'New Telecom Policy: Expectations of Entrepreneurs' organised by the Telecom and Technology Reporters Network Bangladesh (TRNB). He actually viewed that Bangladesh's mobile and broadband internet services ranked among the worst in the world. According to the Global System for Mobile Communications Association (GSMA), and the UN agency, the 'International Telecommunication Union (ITU)', in terms of broadband service quality, Bangladesh ranks 141st among 153 countries of the world. In South Asia, too, we are trailing even behind Nepal and Bhutan. Oddly enough, considering subscription coverage, Bangladesh is among the top 10 countries globally; but when it comes to service, we are among the bottom 10 countries. Small wonder that despite its poor quality, the highly taxed internet service is one of the costliest in the world. However, Mr Asad assured that the government had plans to address such issues by rapidly expanding mobile and broadband infrastructure and thereby connecting 90 per cent of the population with 5G and providing 100 Megabits per second (Mbps) internet to 90 per cent users. No doubt, the government's plan is well-intentioned, though rather ambitious, especially at a time when supply of power and energy to fulfil the government's dream project on internet service is facing the worst challenge in the history. The reason is the ongoing crisis in power generation due to acute shortage of fossil fuels, particularly natural gas and oils. Since the domestic natural gas supply is on the decline and the possibility of improving it is slim at least in the near future, the country will have to depend on imported fossil fuels-liquefied natural gas (LNG), oils and coal- indefinitely. Production of renewable energy is still at the rudimentary stage. But at the moment disruption in the energy supply chain has reached epic proportions, if only due to the ongoing conflict in the Middle East, the chief source of fossil fuels in this part of the world.

Even if it is assumed that the Middle Eastern crisis is not going to persist for eternity, still the magnitude of disruption and dislocation it would cause particularly in the countries dependent on energy import is going to be humongous. And it is exactly against this backdrop that the Association of Mobile Telecom Operators of Bangladesh (AMTOB), hardly a week back, in a letter to the telecom regulator, Bangladesh Telecommunication Regulatory Commission (BTRC), called upon the authorities to act urgently to resolve the deepening electricity and fuel crisis and, unless addressed on a emergency basis, it (AMTOB) warned of an imminent nationwide telecom disruption. Especially, it raised serious concerns about the vulnerability of data centres as well as the wider digital economy. In fact, there are already reports of prolonged outages. In some cases, the power goes out for long five, even eight hours during natural calamities like storms, when the telecom operators are compelled to depend on diesel generators to keep their critical infrastructures running. In this connection, mobile operators informed that their 'base transceiver stations (BTS)' alone consume more than 52,000 litres of diesel and about 20,000 litres of octane in a day across operators. Notably, a BTS is a critical piece of networking equipment that enables wireless communication between mobile devices such as phones and a cellular network, typically used in 2G, 3G and 4G networks. Obviously, any shutdown of such critical infrastructures, the mobile operators warned, would disrupt country's emergency services including disaster response, financial transactions, law-enforcement coordination, digital governance and overall economic activities. So, one can easily imagine the cascading effects of data centres going offline in absence of the required fuel supply. For instance, such eventuality would result in widespread call drops, internet outages and service blackouts. One might recall here what happened during the more than two weeks' internet shutdown effected by the then-government in reaction to 2024's student-led July protests. The fallout of that nationwide communication blackout, an instance of wilful internet shutdown, disrupted financial transactions, normal business, education, in short, it impacted every sphere of life.

But here we are talking about a situation that is beyond the control of the government. So, how is the government going to meet the competing demands for fuels from power plants, the transport sector, industries and now the critical internet infrastructures given that the existing stock of fuel is limited and that the import of fuels is facing uncertainties due to the war? That brings us to the basic issue of setting priorities and planning any ambitious project accordingly. The basic condition for operating any industry is uninterrupted supply of power. The telecom sector is no exception. But over the years, the governments that came to power could not address this fundamental requirement of the industries, let alone public life, which is entirely dependent on a secure supply of power. But without a long-term planning to develop a secure and sustainable energy base, ambitious projects had from time to time been undertaken to impress the public at best and lining the pockets of those in power at worst. So, in the current situation, the government in office, has, to be fair, no shortcut answer to the worsening energy crisis facing the economy. What the government can do at best is prioritise the sectors of the economy in desperate need and allocate the energy in stock among them judiciously. And at the same time, pray that the warring parties in the Middle East settle for peace within a short time. But assuming that the present shock of global energy crunch is going to be over shortly, can we still expect that the incumbent government would be able to resolve the ongoing power crisis any time soon? Not in the least. While the government continues to meet emergencies through judicious allocation of resources, the long-term plan for securing a sound energy base should remain the top priority. And it is exactly on this sound energy base hinges the incumbent government's dream of creating what it would like to term ' AI-enabled Bangladesh'. Since AI is a power-guzzling technology, the government should set its priorities right to avoid jumping the gun.​

 

[Saif]

The urgent need for AI literacy in Bangladesh’s schools

On a rainy autumn evening, a seemingly ordinary moment revealed an increasingly common reality. A younger student, still in the early years of secondary school, was seen using artificial intelligence with remarkable ease to complete assignments and solve mathematical problems. Such technological...

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The urgent need for AI literacy in Bangladesh’s schools

Nafew Sajed Joy
Published: 27 Apr 2026, 12: 52

A digital multimedia classroom in Bangladesh File photo

On a rainy autumn evening, a seemingly ordinary moment revealed an increasingly common reality. A younger student, still in the early years of secondary school, was seen using artificial intelligence with remarkable ease to complete assignments and solve mathematical problems. Such technological fluency is, on the surface, impressive. Yet it also raises a deeper concern. The scene echoes an earlier transition, when the introduction of calculators into classrooms quietly began to erode foundational mathematical skills among students who had not yet mastered the basics.

There was a time in Bangladesh when memorising multiplication tables was not merely an academic exercise but a shared family ritual. Parents took pride in ensuring their children internalised these fundamentals. The gradual departure from that discipline, often in the name of modernisation, came at a cost.


Many who embraced technological shortcuts too early later found themselves struggling with more complex problem-solving, having missed the critical stage of foundational learning. Technology, while undeniably beneficial, demands a careful understanding of when and how it should be used.

This is not to suggest that artificial intelligence has no place in daily life. On the contrary, its utility is undeniable. Few would argue against using AI to draft routine emails or assist with time-sensitive tasks. However, such use presupposes a level of maturity and discernment that is often absent among younger users. Increasingly, even trivial decisions, from clothing choices to simple queries, are being outsourced to AI. What appears to be harmless convenience may, in fact, signal the beginning of a deeper dependency.

The implications of this reliance extend beyond efficiency. Frequent dependence on AI for tasks such as translation or writing can gradually diminish an individual’s willingness and ability to perform these tasks independently. Over time, what was once a skill becomes perceived as burdensome. More subtly, patterns of thought, expression, and even creativity begin to mirror the outputs of machines. This transformation is not always immediately visible, particularly to younger users who experience only the benefits of ease and speed.

The concern, therefore, is not about technology itself but about its uncritical adoption. For a generation growing up with AI, the distinction between assistance and dependence is becoming increasingly blurred. Convenience, if left unchecked, risks eroding the very skills that education seeks to cultivate, like critical thinking, creativity, and independent problem-solving. In such a landscape, technology ceases to be a tool and instead becomes the driver, reducing human agency to that of a passive passenger.

According to UNESCO, 84 per cent of countries still lack comprehensive laws to protect students’ data, heightening privacy risks, while AI systems continue to exhibit racial and gender bias.
As artificial intelligence continues to integrate into everyday life, the challenge lies in striking a balance. The question is no longer whether to use AI, but how to ensure that its use enhances, rather than replaces, the intellectual and creative capacities of individuals. Without this balance, the long-term cost of short-term convenience may prove far greater than anticipated.

This is why AI literacy and awareness about its responsible use, is now essential for teachers, students, and parents alike. Teachers need to understand how students are using AI and guide them in the right direction. Parents must ensure that their children learn to control technology rather than letting it control them.

Schools in Bangladesh are already adapting to the rapid rise of artificial intelligence in education, especially through tools like ChatGPT, which are transforming teaching and learning methods. This transformation brings great promise but also serious risks. AI can make learning more engaging and accessible, but it also raises concerns about academic integrity, over-dependence, and the erosion of critical thinking.

Many teachers are struggling to strike a balance between innovation and ethical use, particularly in schools that lack adequate resources or AI detection tools. Rather than resisting AI, it must be integrated responsibly: by training teachers properly, encouraging students to think critically instead of copying blindly, and reforming evaluation systems to prioritize creativity, understanding, and honesty.

Artificial intelligence is now reshaping education, governance, and society worldwide, including in Bangladesh. Yet its misuse, through deepfakes, misinformation, fraud, and cybercrime; threatens public trust and social stability. Without a coordinated framework for AI governance, Bangladesh faces risks of confusion, electoral interference, and ethical breaches across media and education.

The children in schools today have no idea of the magnitude of this threat. Their minds are not yet ready to grasp it.

Globally, the AI-driven education market is projected to exceed 20 billion USD by 2027. Many countries are redesigning curricula, training teachers, and addressing ethical challenges to help students grow as creative and critical thinkers in an algorithmic era.

China plans to make AI education compulsory from primary to secondary level by 2025. South Korea is introducing AI-powered digital textbooks with government funding. Australia emphasises responsible use, Estonia’s KrattAI initiative teaches ethical AI to children aged 7–19, India’s YUVAi program integrates technological and socio-emotional learning, and the UAE aims to train one million people by 2027. Finland’s Elements of AI course has already reached over 1.2 million learners worldwide.

Hong Kong has launched a mandatory 10–14 hour AI curriculum that includes algorithmic fairness and generative AI. However, according to UNESCO, 84 per cent of countries still lack comprehensive laws to protect students’ data, heightening privacy risks, while AI systems continue to exhibit racial and gender bias.

Dr. Samar Fatima’s paper “Teaching in the Age of Artificial Intelligence (AI)” analyses how AI is reshaping teaching and learning through personalized education, automated assessments, chatbots, predictive analytics, intelligent robots, and VR/AR technologies. This study shows that AI enhances student engagement, reduces teachers’ workload, and encourages individualized learning, but it can never replace the human element of education. Teachers remain central to guiding students in using AI ethically and effectively. This study also warns of serious risks, including data privacy issues, unequal access to technology, over-dependence, and algorithmic bias.

This paper argues that both teachers and students must achieve technological and ethical AI literacy to ensure responsible use.
From the lessons we have learned, it is clear that textbooks from sixth to twelfth grade of Bangladesh should include introductory chapters on the proper use of AI, outlining what can and cannot be done. At the senior level, AI could even be introduced as a separate subject to help students adapt to the modern world without losing their creativity. Equally important are workshops for teachers and parents, enabling them to monitor students’ use and provide proper guidance. Alongside this, nationwide awareness campaigns should be launched to promote responsible and informed use of AI.


Finally, establishing a National AI Ethics and Safety Commission, enforcing watermarking and clear labeling on AI-generated content, and promoting AI awareness nationwide are now urgent needs. Through reforms in education, policy, and law, we must ensure that AI remains a tool for innovation, not for control. Bangladesh must act decisively now, before AI begins to control us.

* Nafew Sajed Joy is a writer and researcher.​

 

[Saif]

Why AI alone cannot fix Bangladesh’s learning gap

AI technology alone will never resolve Bangladesh's learning gap without consistent human support from teachers and parents.

www.thedailystar.net

Why AI alone cannot fix Bangladesh’s learning gap

29 April 2026, 13:58 PM
UPDATED 29 April 2026, 13:58 PM

Md Maruf Hasan

Bangladesh’s education system has experienced waves of disruption over the past five years, especially since the pandemic. Students and teachers have had to cope with online classes, adapt to the 2021 curriculum framework, and then revert to the 2010 curriculum, often without clarity on where the system is headed or what education will look like in the next five years.

Meanwhile, many countries are trying to make the most of AI, using it as a data-driven decision-making tool to reduce pressure on teachers.

However, within this instability in Bangladesh’s education system, the people who are suffering the most are our children.

Their learning journeys are increasingly marked by confusion. Many have progressed through school during a period when high-stakes examinations were reduced, intended to ease pressure. Yet an unintended consequence has been a growing gap in foundational learning, especially for first-generation learners in rural Bangladesh.

In 2026, it is not uncommon to see reports of SSC graduates demonstrating competencies closer to Grade 7 levels.

In 2025, as a STEM teacher at a rural high school in Ukhiya, Cox’s Bazar, I sought to address this gap by developing an AI-powered Bangla-language mathematics app. The idea was simple. Students could use their smartphones at home to work on gaps in their learning.

However, the students did not use the app as much as expected.

That was when I realised something deeper.

The issue was not access to technology. It was the absence of a supportive learning environment.

In my experience, underserved students often lack structured academic support at home. This is not because families do not value education, but because they do not know how to support their children. Many of these students are the first in their families to attend school.

A meaningful learning environment requires consistent guidance from both teachers and parents. Without that, even the most well-designed digital tools struggle to make an impact.

This insight led us to take a different approach.

Instead of focusing only on content delivery, my students and I co-created an app called Jibon Shikkha. The goal was not just to teach subjects, but to support daily habits and routines.

The real gap is not technological. It is human.
Students input data about their daily lives, including study hours by subject, food habits, sleep quality, mood, energy levels, school attendance, and playtime. The AI system then provided simple, actionable guidance on what was going well, what could be improved, and the next steps.

Before launching the app, we worked with experts to learn about self-management, health, and time management. Students also engaged their parents through sessions and shared how they could support their children.

This changed everything.

Self-monitoring habits, guided by parents, increased from below 30% to over 75%. School attendance improved. In total, more than 2,000 students and over 6,000 community members were engaged through this process.

This experience makes it clear that AI in education cannot be reduced to tools that deliver content alone.

Visual: Star.


Digital solutions require sustained investment, not just in technology, but in people. Students, teachers, and parents all need the capacity to engage meaningfully with these tools.

At a time when Bangladesh’s education system is already experiencing frequent changes, the challenge is not simply to introduce AI, but to integrate it thoughtfully into learners’ realities.

The real gap is not technological. It is human.

MD Maruf Hasan is an AI in Education Practitioner who works with AI to develop learning strategies and tools for Teach for All and Anthropic.​

 

[Saif]

The comfort of AI, the absence of us

Do we agree on something like AI listens to us without interrupting? We have so many thoughts about our day-to-day life. Even the slightest misunderstanding or something that has bothered us, we have a lot of questions regarding the incidents. ‘Why?’ or ‘Did I do something, or is it something...

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The comfort of AI, the absence of us

Tasmia Sristi
Published: 03 May 2026, 19: 43

An AI (Artificial Intelligence) sign is seen at the World Artificial Intelligence Conference (WAIC) in Shanghai, China 6 July, 2023. File photo

We have so many thoughts about our day-to-day life. Even the slightest misunderstanding or something that has bothered us, we have a lot of questions regarding the incidents. ‘Why?’ or ‘Did I do something, or is it something else?’ etc.

Heart may be in a dilemma, but the brain keeps thinking, why not ask someone about it to get a clear picture, or to get support. However, we usually would not like to bother someone else with a silly matter. Because the brain keeps thinking, ‘Why would I want to be known as a silly person? They might think I am a bit too emotional. Better, share my thoughts with AI?’

Thinking like this is not completely harmful. Because AI helps in taking a clear picture of what we are bothered by, giving us suggestions based on it, trying to say what specific things might help to resolve something, etc. But if we think in a different pattern, it might be shocking.

Let us hypothetically think of someone who just had a worst misunderstanding with their closest person. And usually, she would share thoughts with someone who would listen, and suggest something to her, console her. But in terms of this matter, she is not comfortable enough to share with someone else and does not want to create any drama or anything.

She keeps thinking about it a lot and wants to get things out of her head. Got the idea to slightly share it with AI. AI did its job, got her a clear picture of the incident, said what she could do afterwards, and said a few sentences to console her as well. But this is not the end. This becomes a pattern; anything that happens in her life that makes her emotionally bad, she goes back to AI. Day after day, she seeks this dependency through AI, not from any human being, activities, pets, or anything that has helped her before.

AI is here for our help, but excessively depending on and misusing it for our education or work-related matters is a concern. Being emotionally dependent on it is much more dangerous if we deeply think about it.

From a quiet observation and listening to others' experiences, it has been noticed that when no one is available or does not feel anyone would understand, people are venting to AI. Happening because they are feeling AI as a safe place, a great listener, and advisor. Keep going back to it to get comfort, validation, or even for decision-making. Asked why they feel like AI should be taking this place, answers included, how people are being judged or unheard in real life? Also, AI is always available, not judgmental, and very patient. Because of the digital lifestyle, being socially isolated and causing emotional fatigue, this is occurring as well.

We have long seen films and series imagine a future where humans grow emotionally attached to machines. Episodes of Black Mirror once felt unsettling for how they portrayed people withdrawing from real-life connections in favour of artificial ones. But now, seeing the pattern within us, we are asking ourselves questions, such as, is this just an encouraged understanding or a real connection? Also, the thought of whether we are trying to avoid difficult relationships with humans. Or what if this constant emotional dependency on AI makes our emotional resilience weaker?

These are crucial to think about. Yes, AI is here for our help, but excessively depending on and misusing it for our education or work-related matters is a concern. Being emotionally dependent on it is much more dangerous if we deeply think about it. Not ignoring the fact that AI can help people for some time when they are feeling alone in this busy world, accessible and helpful when no one is there. But the pattern of constantly happening is like, maybe AI is quietly limiting us.

So, if we begin to rely on AI to hold our emotions, what will happen to the difficult, messy, but deeply humans act towards each other? Maybe this is what we need to think about now.

#Tasmia Sristi is a student of Masters in Communication, ULAB​

 

[Saif]

Bangladesh could lose 5.6m jobs due to AI expansion: Debapriya

Prominent economist Debapriya Bhattacharya on Thursday warned that artificial intelligence, robotics and rapid technological disruption could eliminate as many as 5.6 million jobs in Bangladesh, raising urgent questions about the country's readiness for a fast-shifting labour market. Speaking...

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Bangladesh could lose 5.6m jobs due to AI expansion: Debapriya

Debapriya Bhattacharya Prothom Alo file photo

Prominent economist Debapriya Bhattacharya on Thursday warned that artificial intelligence, robotics and rapid technological disruption could eliminate as many as 5.6 million jobs in Bangladesh, raising urgent questions about the country's readiness for a fast-shifting labour market.

Speaking at a dialogue titled “Government Priorities and the Education Sector: Budget and Reality” arranged by Citizen's Platform for SDGs, Bangladesh at the Bangladesh-China Friendship Conference Centre in Agargaon, he said the challenge runs deeper than job losses alone.

“AI could eliminate 5.6 million jobs in Bangladesh. Even if 5 million technology-driven jobs are created in their place, the real question is whether Bangladesh has prepared itself to compete for them,” said Debapriya, Centre for Policy Dialogue (CPD) Distinguished Fellow.

He stressed that the country's young workforce faces a severe skills and training deficit that could lock them out of emerging employment opportunities, regardless of how many new positions the digital economy generates.

Shifting focus to education policy, the economist argued that the national priority must move beyond access to quality and measurable outcomes.

“Students are enrolling in schools, but what standard of learning are they leaving with? That is now the defining question,” he said, calling for a broad-based movement around outcome-linked accountability in education.

Debapriya noted that discussions were underway to transform the education agenda into a political and social movement, including a proposed joint initiative with newly elected members of parliament. “We believe what Bangladesh needs most right now is an education movement. Whenever we have gone to people ahead of elections and asked about their aspirations, quality education has always topped their list.”

On the government's stipend programme, he cautioned that financial transfers alone cannot address the true cost burden faced by poor and marginalised families. “Stipends help, but they fall far short of covering all the expenses that make education unaffordable for disadvantaged households.”

The economist framed the country's educational imperative in a shift of strategic emphasis from the earlier goal of universal enrolment to a new mission of ensuring learning outcomes. “We once fought for access to education. Now we must fight for the outcomes of that education.”

He also underscored that meaningful reform in education requires political and social mobilisation, not just technical recommendations, and that sustaining Bangladesh's development trajectory would be impossible without a qualitative leap in its education system.

State Minister for Primary and Mass Education Bobby Hajjaj attended the event as the chief guest, while CPD Additional Director (Research) Towfiqul Islam Khan delivered the keynote paper.​

 

[Saif]

The Financial Express | First Financial Daily of Bangladesh

Get latest stock share market news, financial news, economy news, politics news, breaking news, Bangladesh economy news at The Financial Express.

thefinancialexpress.com.bd

Govt plans AI use to tackle market syndicates

bdnews24.com

Published :
May 11, 2026 19:11
Updated :
May 11, 2026 19:11

The government is planning to deploy artificial intelligence (AI) to monitor supply chains and dismantle market syndicates responsible for price-gouging of essential commodities.

Commerce Minister Khandakar Abdul Muktadir announced the initiative on Monday during the inauguration of the Trading Corporation of Bangladesh (TCB) truck sale initiative in Dhaka’s Agargaon.

“To ensure that no kind of syndicate or manipulation can become effective in the market for essential products, the government has taken initiatives including strategic reserves, AI-based supply chain monitoring and enhancement of TCB’s capacity,” the minister said.

He said the measures would allow closer monitoring of market conditions and supply chains.

According to the minister, imports of essential commodities are naturally dominated by some large businesses because the sector is capital-intensive.

“However, the government wants to build a partnership-based system so that more entrepreneurs can participate in this sector and healthy competition can develop in the market,” he said.

TCB’s truck sale programme before Eid-ul-Azha has started in Agargaon.

The commerce minister said the government’s main objective was to ensure subsidised essential food products for low-income, lower-middle-income and marginalised people.

“To achieve this goal, products are being sold regularly at subsidised prices across the country through TCB, while truck sale programmes are also being conducted around special festivals,” he said.

He said around 22.8 million people received subsidised food products during Ramadan, and the number is now being increased to nearly 28.8 million before Eid-ul-Azha.

“During last year’s Eid-ul-Azha, nearly 10,900 tonnes of food products were supplied. This year the amount has been increased to around 14,000 tonnes, which is nearly 40 percent higher,” he added.​

 

[Saif]

Stop taking health advice from AI-generated social media videos

Why AI health videos mislead with simple certainty

www.thedailystar.net

Stop taking health advice from AI-generated social media videos

Ahnaf Tahmeed Purna

Photo: Orchid Chakma

A cartoon garlic clove aggressively scrubbing toxins off of blood vessels. A bright yellow turmeric root polishing the liver. Spinach being dragged across the colon like a sponge wiping everything clean.

You’ve seen these short videos. Everyone has. These AI-generated videos are designed to be oddly satisfying. Clean visuals, simple ideas, and a reassuring sense that health is just a matter of “cleansing” the body the right way. One food, one organ, one problem solved.

But what makes them effective is exactly what makes them misleading. They take real human anatomy and turn it into a cartoon system of dirt and detergents. They replace medical complexity with visual certainty, where turmeric is not a spice with mild anti-inflammatory properties, but a liver “detox tool”, and garlic is not a food with limited cardiovascular associations, but a direct pipe-cleaner for arteries.

As a medical student, one of the lessons you learn in your early years is that the human body seldom behaves in simple, linear ways. There is no single food that “cleans” an organ, no universal detox pathway that can be activated through dietary hacks, and no shortcut that bypasses physiology. Yet, social media thrives on exactly the opposite idea: health is a puzzle with easy answers waiting to be unlocked. It is emotionally entrancing. It gives viewers a sense of control in a system that often feels overwhelming. This is where AI-generated content fits in. It does not just spread misinformation; it packages it in a format that feels intuitive and passively rewires how people understand their own bodies.

The danger today is not only that false health advice exists online, but that it is increasingly being generated at scale by systems that are not accountable to evidence. AI tools can replicate the tone of educational content while stripping away the safeguards of medical accuracy. Combined with animation tools and content templates, this results in a flood of videos that look like simplified medical education but are often detached from clinical reality.

In clinical practice, one pattern physicians notice is that people interpret symptoms through whatever information is most accessible to them at the time. Increasingly, today, that information is coming from short-form social media content rather than trained professionals. A persistent headache becomes “toxins”. Fatigue becomes “deficiency”. Digestive discomfort becomes a “colon cleanse issue”. By the time real medical attention is sought, the narrative has already been shaped, sometimes in ways that delay proper diagnosis. This does not happen because people are careless. It happens because the content is persuasive, visually clear, and emotionally reassuring. It offers certainty where medicine often cannot.

What makes AI-generated health content particularly difficult to challenge is its tone. It sounds confident, structured, and explanatory. In medicine, however, confidence is not the same as facticity. Real clinical guidance is always cautious, conditional, and inherently context-dependent. It does not promise universal fixes because it cannot. But caution does not perform well on social media; certainty does. And so, that certainty gets amplified, even when it is unsupported.

It would be easy to place all responsibility on viewers, but that would be incomplete. Platforms are designed to maximise engagement, not accuracy. AI-generated content is cheap, fast, and scalable. Medical correction is slow, nuanced, and often ignored in algorithmic systems. This creates a structural imbalance: evidence-based medicine competes with content optimised for attention. The result is not just about spreading misinformation but rather about misinformation outperforming information.

The solution is not to disconnect from digital platforms entirely. They are now part of how people learn and communicate. But there needs to be a stronger culture of scepticism when it comes to health content, especially content that feels too simple.

A useful question we can ask ourselves is, does this explanation acknowledge complexity, or does it discard it? Real medicine rarely fits into a single cause-and-effect story. When something claims to do so, it deserves scrutiny.

Equally important is where we choose to get medical understanding from. Not all information sources carry the same weight. Peer-reviewed literature, trained clinicians, and established medical institutions exist for a reason. They are built on systems of validation, emendation, and accountability.

There is an implicit risk in the way health information is evolving online. It is not just that false claims are circulating. It is that the boundary between education and entertainment is blurring. When a cartoon vegetable can convincingly “clean” your colon in 30 seconds, and when that feels more intuitive than actual physiology, we are no longer just dealing with misinformation. We are dealing with a shift in how people understand their own bodies.

Medicine is not a set of visual metaphors nor a collection of easy fixes. It is a discipline where patterns are interpreted through evidence, probability, and clinical judgment rather than certainty or shortcuts. And the more we replace that complexity with algorithm-friendly simplicity, the further we drift from what a genuine understanding of health is supposed to be.

Purna is a fourth-year medical student at Sirajganj Medical College.​

 

[Saif]

Doctors can't be replaced: Why we need an “arguable” AI partner

With a national shortage in Bangladesh of over 90,000 doctors, any technological solution we introduce must solve this crisis rather than worsen it.

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Doctors can't be replaced: Why we need an “arguable” AI partner

Farjana Yesmin
Updated: 15 May 2026, 13: 35

Doctors wearing vr simulation with hologram medical technologyrawpixel.com / roungroat

I remember the long, quiet queues at the village clinics in Satkhira where I grew up. Families waited for hours under the heat, speaking in low voices, hoping for a few minutes with a doctor who was likely exhausted. In those settings, the primary challenge is not a lack of data. It is a lack of time.

Bangladesh currently has roughly 0.7 physicians and less than one hospital bed for every 1,000 people. These numbers fall far below World Health Organization recommendations. With a national shortage of over 90,000 doctors, any technological solution we introduce must solve this crisis rather than worsen it.

In late April 2026, Google DeepMind announced its "AI co-clinician" research initiative. This vision suggests a "triadic" model of care: an AI agent working alongside both the patient and the doctor. It is designed to extend a clinician’s reach while keeping the human expert in control. While this is a significant technical achievement, we must ask a difficult question: If these systems are built for well-resourced hospitals in the Global North, will they truly help a rural health complex in Bangladesh, or will they create new forms of inequality?

The vision of a reasoning partner

The DeepMind initiative moves away from the old idea of AI as a simple "black box" that gives a single answer. Their system was tested using the "NOHARM" framework, which focuses on avoiding errors of omission and commission. In blind evaluations, physicians often preferred its synthesized evidence over traditional tools.

This reflects a shift from AI as a judge to AI as a collaborator. It is a principle Bangladesh should endorse, but with caution. A generic "co-clinician" designed in London or California may not understand the messy, fragmented reality of our local healthcare system.

Why collaboration is not enough

In my research on "arguable systems," I have argued that a co-clinician should do more than just offer a recommendation for a doctor to accept or reject. True collaboration requires the ability to disagree.

A doctor in a busy upazila health complex might need to argue with the AI. The patient in front of her might come from a community the AI never encountered during its training. They might speak a dialect the model cannot parse or present symptoms that do not fit the clean patterns of Western datasets.

When an algorithm fails to listen, it commits what philosophers call "epistemic injustice." This happens in two ways. First, "testimonial injustice" occurs when the AI fails to trust what a patient knows about their own body because that patient is not from a wealthy or digitally recorded demographic. Second, "hermeneutical injustice" happens when the patient lacks the specific vocabulary the AI expects. A truly collaborative system must be designed to notice these gaps and make its own uncertainty visible to the doctor.

The privacy-explainability tension

Bangladesh is currently developing its digital health infrastructure and data protection laws. Our hospitals cannot simply pool patient records into a central server for legal and ethical reasons.

Federated learning offers a solution by allowing hospitals to train shared models without moving raw patient data. My work on the MedHE framework uses encryption to create a "fortress" for patient privacy. However, there is a hidden cost: the "noise" used to protect privacy can sometimes hide rare diseases or the early signals of an outbreak.

When we build or deploy these models, we should not only ask if they are accurate. We must ask if they listen. We must ensure that when a patient’s life depends on it, the doctor remains the final authority, supported by a partner that knows its own limits.

In Bangladesh, if privacy settings are too aggressive, a dengue early-warning system might fail to detect a cluster in a remote district because that cluster appears as statistical noise. We need "equity-aware" privacy. The goal should not be mathematical purity, but clinical truth for the most vulnerable populations.

Bias as a local reality

We often treat algorithmic bias as a technical bug, but it is actually a reflection of the training environment. An ECG-based heart disease predictor trained mostly on men may systematically under-diagnose women who show different symptoms.

In my research on fairness-aware representation learning, we train models to ignore sensitive characteristics like gender or income when they lead to discriminatory outcomes. For a co-clinician to work in Bangladesh, it must be evaluated on our own patient populations and our own languages. Fairness cannot be an afterthought.

Bangladesh as a research partner

Our country is not a passive recipient of Western technology. Local researchers are already building solutions that fit our specific context.

In a study I conducted with 14 healthcare professionals in Bangladesh, we found that clinicians strongly preferred "hybrid" explanations. These systems combine data-driven insights with established medical rules. More than half of the clinicians expressed trust for actual clinical use because they could see the logic behind the suggestion.

From multilingual triage apps to AI-assisted referral systems for pregnant women, a local ecosystem is growing. Our policymakers should treat Bangladesh not as a testing ground for foreign models, but as a partner with its own research capacity.

A roadmap for the future

To move forward, I propose five essential steps for integrating AI into our clinics:

1. Demand arguability: Systems must allow doctors to contest reasoning and see structured uncertainty. A doctor should be able to argue with the AI and win.

2. Local fairness benchmarks: We need our own test sets and definitions of what constitutes a harmful error in a rural setting.

3. Equity-aware privacy: We must ensure that privacy protections do not erase the signals of marginalized communities or rare conditions.

4. Invest in collaboration research: We need large-scale trials that measure trust and workflow integration, not just technical accuracy.

5. National AI audit body: A technical and ethical board should monitor algorithms before and after deployment to prevent bias or privacy breaches.

It's about the patient's life

I became a research scientist because I wanted to build technology that finally hears the voices I heard in those Satkhira queues. The DeepMind co-clinician is an impressive tool, but its success in Bangladesh will depend on whether it can adapt to us.

When we build or deploy these models, we should not only ask if they are accurate. We must ask if they listen. We must ensure that when a patient’s life depends on it, the doctor remains the final authority, supported by a partner that knows its own limits.​

 

[Saif]

Beyond chatbot: Why 2026 is the year of 'Agentic' AI

As students standing on the precipice of the workforce, this is the trend we actually need to talk about. It is no longer just about text generation; it is about autonomous execution.

en.prothomalo.com

Beyond chatbot: Why 2026 is the year of 'Agentic' AI

Nowshed Alam

Figurines with computers and smartphones are seen in front of the words "Artificial Intelligence AI" in this illustration taken, 19 February 2024.Reuters file photo

Walk into any university library or coffee shop right now, and you will see the same thing: students with split screens, typing furiously while a chatbot helps them debug code, summarise a dense 50-page reading, or structure an essay outline. For the past few years, this has been our relationship with Artificial Intelligence. It was a highly advanced, ultra-obedient digital assistant. You give a prompt; it gives an answer.

But if you have been paying attention to the tech landscape over the last few weeks, you might have noticed a massive vibe shift. We are quietly moving away from the era of the chatbot and entering something entirely different: the era of Agentic AI.

As students standing on the precipice of the workforce, this is the trend we actually need to talk about. It is no longer just about text generation; it is about autonomous execution.

From assistants to operators

To understand why this is a big deal, we have to look at how the latest models—like OpenAI’s GPT-5.5 or Anthropic’s "Mythos" framework—are being deployed.

Until recently, AI lacked a sense of continuity. If you wanted it to research a topic, write a report, create a spreadsheet, and send an email, you had to manually guide it through every single step. You were the manager, and the AI was an intern taking literal, isolated instructions.

"Agentic AI" changes the dynamic entirely. Instead of giving the system a specific prompt, you give it a goal.

The shift: Instead of asking an AI, "Write a Python script to scrape this website," you can now tell an Agentic system, "Look at our club's budget spreadsheet, find where we are overspending on event catering, research three cheaper local alternatives, and draft a polite email to the executive team proposing the switch."

The AI agent then hooks into various APIs, browses the web, modifies files, and executes the multi-step workflow entirely on its own. It doesn’t just answer questions anymore; it takes initiative. Tech analysts are already predicting that by the end of this year, multi-agent systems will be running uninterrupted, eight-hour workstreams with zero human intervention.

The open-source democratisation

What makes this momentum feel so explosive right now is that it isn’t just locked behind the trillion-dollar walls of Silicon Valley. Ever since the "DeepSeek moment" earlier last year proved that world-class models could be built efficiently without astronomical budgets, open-source AI has gone completely viral.

For university students and independent developers, this is incredibly empowering. We are seeing a massive surge of highly optimised, smaller models that can run directly on a laptop or smartphone (Edge AI) without needing constant internet access. The barrier to entry to build an autonomous agent has completely melted away. You don’t need a massive tech budget anymore; you just need a good idea and a weekend to experiment.

The reality check: Security and accountability

Of course, this sudden leap into autonomy is causing a bit of a panic at the institutional level. Over the past month, the US government and various regulatory bodies have stepped in, demanding mandatory pre-release security testing frameworks for these frontier models.

It makes sense. When an AI can autonomously scan code, navigate financial networks, and make decisions, it stops being a typing tool and becomes a piece of critical infrastructure. Anthropic’s recent models, for example, have reportedly been uncovering decades-old bugs and vulnerabilities in legacy banking systems that human auditors missed for years.

If an autonomous agent makes a catastrophic error, or compromises sensitive data while trying to optimise a workflow, who is to blame? The developer? The user? The company that hosted the model? These are the ethical and legal questions our generation will have to solve.

What this means for our future

There is a running joke among my peers that we are studying for degrees in jobs that might look completely unrecognisable by the time we graduate. And honestly, it is a valid fear. If AI can independently manage workflows, write code, and analyse data, the entry-level corporate landscape is going to shift dramatically.

But looking at this trend closely, it doesn't mean human skills are becoming obsolete—it means they are being recalibrated. The focus is shifting away from routine execution and moving toward strategy, critical thinking, and orchestration.

We don't just need to learn how to do the work anymore; we need to learn how to guide the systems that do the work. The future is not about competing against AI agents; it is about learning how to manage them.

Nowshed Alam is a Computer Science and Engineering student at the University of Asia Pacific (UAP) in Dhaka.​

 

[Saif]

AI won’t take your job, but the person using it efficiently might

As AI takes over routine tasks, the real advantage will belong to professionals who learn to think sharper, ask better questions, and use the technology as a partner rather than fear it as a rival

www.thedailystar.net

AI won’t take your job, but the person using it efficiently might

As AI takes over routine tasks, the real advantage will belong to professionals who learn to think sharper, ask better questions, and use the technology as a partner rather than fear it as a rival.

Rashed Noman

Illustration: Courtesy

In 1997, a computer named Deep Blue did the unthinkable by defeating world chess champion Garry Kasparov. At the time, the headlines read like a eulogy for human relevance, declaring the match "The Human Brain’s Last Stand." There was a collective chill in the air; we assumed that once the machine won, the spirit of human ingenuity would simply wither away.

Yet, nearly 30 years later, chess is more vibrant than ever. But there is a beautiful, ironic twist. The strongest player on earth today is neither a cold machine nor a lone human genius. It is a human playing in harmony with a computer. This partnership, known as "Cyborg Chess," was a vision championed by Kasparov himself, the very man who first felt the sting of that historic loss.

Today, we are facing our own "1997 moment." The game is no longer played on a checkered board; it is being played out in our careers. I know there is a heavy, quiet anxiety in the air: the fear that AI is coming to take our seats at the table.

But let’s look closer. Imagine two colleagues working side-by-side. One spends half their day drowning in "scaffolding", drafting routine emails, scouring data, and manually organising. They only have a few hours left for actual dreaming and discovery. The other professional uses AI to clear those hurdles in minutes, reclaiming their entire day for deep strategy.

We must be honest with ourselves: No algorithm is going to take your job. But your job might be taken by the person who realises that AI is not a threat, but a way to finally breathe and focus on what truly matters.

For decades, we treated technology as a silent tool. A hammer does not have an opinion, and a spreadsheet does not have a vision. But AI has shifted the paradigm. It has become a cognitive partner, a teammate that sits beside us, ready to brainstorm.

In every profession, much of our day is consumed by repetitive, soul-crushing tasks that keep us busy but do not move the world forward. When we let AI handle this routine work, what remains is the most precious part of being human: our ability to see the big picture.

Our work was never supposed to be about how fast we can type or how many rows we can fill. It is about the "actual engineering" of solutions. When the machine handles the noise, the human is finally free to think.

Consider the impact of this shift on our national development. Imagine a young professional stepping into a massive infrastructure project, a new bridge, a naval port, or a land development.

The traditional path involves a month of stress, buried in a cubicle, trying to track heavy machinery across five districts through endless phone calls. By the time the bottleneck is found, the project is already delayed and over budget.

The AI-augmented professional approaches this with a different spirit. They do not start with a spreadsheet; they start with a prompt. They use AI to see through the chaos, identifying logistics gaps in seconds. They didn't spend their energy counting machines; they spent it solving the puzzle.

We are now entering the age of "AI agents" systems that don't just suggest a plan but act on it, updating safety logs and drafting contracts before you’ve even finished your morning tea. In this world, you are no longer a worker lost in the gears. You are a commander, free to find the magic in your work again.

The expert of the future is not necessarily the person who can write the most complex code. They are the curators of ideas.

Rashed Noman. Photo: Courtesy

In the old world, we rewarded the person who had all the answers. In the AI world, we reward the person who knows how to ask the right questions. We call this "prompt engineering," but it is truly the art of clear, thoughtful communication.

Success is no longer about being a walking encyclopedia. It is about being a "master orchestrator" -- someone who takes scattered threads of data and weaves them into a masterpiece. You let the machine handle the volume, while you provide the value.

Some might wonder, "If the machine is so smart, why do they still need me?" The answer is simple: AI is a mirror, not a visionary.

AI lacks intuition. It does not have "skin in the game." It cannot walk onto a site, see a crack in a beam, and feel that nagging, human sense that something is wrong. The time you save with AI is time you must spend on ethics, sustainability, and human impact.

We are in a race, but we are not running against a computer. We are running against an outdated way of thinking. During the First Industrial Revolution, humans were rewarded for acting like robots. But in the Fourth Industrial Revolution, if you act like a robot, you will be replaced.

This era belongs to the dreamers, the creative spirits, and the people with bold ideas that a machine could never conceive.

Do not fear the software; fear staying exactly where you are. This technology is not our replacement; it is our digital partner. Our jobs are not disappearing. They are simply waiting for us to grow into the professionals we were always meant to be. We are the world’s problem solvers, and AI is simply the greatest tool we have ever been given to solve them.

The writer is the Country Director of Augmedix Bangladesh.​

 

[Saif]

Guarding the digital ledger: how multi-layered AI can prevent credit card fraud

As the financial landscape rapidly shifts towards seamless digital transactions, the sheer convenience of modern commerce is being shadowed by increasingly sophisticated risks. While high-profile credit card fraud syndicates dominate the headlines, a quieter, equally destructive issue eats away at

thefinancialexpress.com.bd

Guarding the digital ledger: how multi-layered AI can prevent credit card fraud

Shuvashish Roy and Md Tuhin Rana

Published :
May 24, 2026 00:27
Updated :
May 24, 2026 00:27

As the financial landscape rapidly shifts towards seamless digital transactions, the sheer convenience of modern commerce is being shadowed by increasingly sophisticated risks. While high-profile credit card fraud syndicates dominate the headlines, a quieter, equally destructive issue eats away at the bottom line of banks and merchant platforms: subtle, complex billing discrepancies. Whether it is an accidental duplicate charge or an unverified subscription renewal, these operational discrepancies silently erode consumer trust and force financial institutions to spend millions of taka annually investigating customer disputes. To protect the integrity of the digital economy, traditional defences must evolve beyond simple, monolithic checkpoints to address the full spectrum of transactional risk.

The primary vulnerability of standard financial security systems lies in their rigid, monolithic designs. Most conventional platforms rely on reactive, single-objective models programmed to look only for obvious, predetermined red flags. However, modern financial risks rarely present themselves uniformly. A criminal executing an outright account takeover leaves a very different digital footprint than a merchant system generating an erroneous recurring fee. Because traditional, single-layer models group all these distinct anomalies together, they frequently trigger false alarms for honest consumers while missing nuanced operational errors entirely.

THE INTELLIGENT CREDIT SENTINEL FRAMEWORK: To solve this operational challenge, our research introduces the Intelligent Credit Sentinel, a synergistic, multi-layered machine learning framework designed to act like an entire team of highly specialised security experts. Rather than relying on one general algorithm, this hierarchical architecture dissects each transaction from four distinct angles:

n Layer 1 - The Broad Gatekeeper

The process begins with an unsupervised deep auto encoder. Trained exclusively on historical, legitimate transaction patterns, this screening layer acts as an initial filter. If a new transaction deviates from standard norms - even if the system has never encountered that specific threat before -- it generates a high reconstruction error, flagging the event as an anomaly.

n Layer 2 - The Fraud Specialist

Transactions are then passed to a supervised, highly tuned machine learning model (XGBoost) trained specifically to hunt down the complex signatures of overt theft. This layer rigorously analyses direct risk indicators, paying close attention to critical physical verification mismatches such as failed Card Verification Value (CVV) inputs or missing Address Verification System (AVS) responses.

n Layer 3 - The Billing Auditor

Operating entirely independently of the fraud tracker, a dedicated, highly optimised two-stage model looks specifically for subtle operational mistakes. By evaluating rolling time windows, transaction velocity, and the exact minutes elapsed since a customer's last purchase, this layer successfully separates malicious fraud from quiet billing anomalies such as duplicate merchant processing.

n Layer 4 - The Executive Decision Engine

An intelligent meta-learner synthesises the individual probability scores from the first three layers alongside the financial magnitude of the transaction. Weighing all evidence logically, this final arbiter translates complex probabilities into immediate, automated operational actions: Approve, Flag for Review, or Decline.

PERFORMANCE AND OPERATIONAL IMPACT: The performance metrics of this synergistic architecture clearly demonstrates the immense value of specialised, multi-layered AI. By utilising advanced hyper parameter tuning to manage severe class imbalances, the dedicated billing anomaly layer achieved an exceptional 94 per cent precision rate. In practical terms, this means that when the system flags a billing error, the alert is incredibly reliable, generating almost zero false alarms. When all layers are synthesised by the meta-learner, the complete system achieves an impressive overall recall rate, capturing 82.4 per cent of all high-risk events across the entire platform.

The operational impact of implementing this hierarchical blueprint across the financial sector would be profound. For traditional banks, mobile financial services, and major payment gateways, managing customer disputes is highly resource-intensive, requiring manual human investigation that increases overhead costs. By deploying an automated framework capable of instantly distinguishing between malicious fraud and operational billing errors, financial institutions can immediately triage issues, reserving expensive manual reviews only for highly ambiguous cases.

Furthermore, the high precision rate ensures that legitimate transactions flow without friction, safeguarding consumer relationships from unnecessary account freezes. Embracing this modular, highly interpretable artificial intelligence paradigm is not just a technical upgrade; it is an essential strategic investment for building a resilient, cost-effective, and deeply trusted financial ecosystem for the future.

Dr. Shuvashish Roy, Senior Researcher, Research & Innovation Division, Prime Bank PLC. Md Tuhin Rana, Student, Department of Statistics, University of Dhaka​

 

[Saif]

The OpenAI–Musk dispute: Who controls the future of AI?

The ongoing dispute between OpenAI and Elon Musk has been framed, at least superficially, as a legal and personal confrontation—a founder challenging the trajectory of an institution he helped to create.

www.thedailystar.net

The OpenAI–Musk dispute: Who controls the future of AI?

Faridul Alam

Elon Musk seen during the trial over the future direction and governance of OpenAI PHOTO: REUTERS

The ongoing dispute between OpenAI and Elon Musk has been framed, at least superficially, as a legal and personal confrontation—a founder challenging the trajectory of an institution he helped to create. The superficiality lies in the tendency to treat proceedings before a US federal court—and any potential jury verdict—as dispositive in the narrow legal sense of conclusively settling a matter. Such outcomes, however, are better understood within what Michel Foucault would call a dispositif: a broader apparatus of power and knowledge in which legal judgments function not as endpoints but as moments within an evolving field of institutional and discursive forces. In other words, a different sequencing before the court, or a variation in how the case was constructed, could just as plausibly have yielded an antithetical result. Headlines that cast the episode as a win or loss for either side therefore risk mistaking procedural resolution for substantive closure, obscuring the more consequential reality: this is not simply a conflict over contracts or corporate governance, but a struggle over the meaning of “openness” in an era in which it is both under assault and structurally difficult to sustain.

At stake is the evolution of artificial intelligence from an aspirational public good into a capital-intensive strategic infrastructure. Musk’s critique rests on the claim that OpenAI has departed from its original nonprofit, open ethos. OpenAI, for its part, has implicitly argued that the scale, cost, and risks associated with developing frontier AI systems require new institutional forms—hybrid models that combine mission with market discipline, and openness with controlled deployment. The disagreement, then, is less about whether OpenAI has changed than about how that change should be interpreted.

A useful contrast can be found in the Human Genome Project, often cited as a model of large-scale scientific openness. Completed in 2003, the project made a deliberate commitment to keeping genomic data in the public domain, resisting efforts to privatise genetic sequences. Yet even there, openness was not uncontested. The publicly funded initiative operated alongside private ventures, most notably Celera Genomics, which sought to commercialise genomic data through proprietary databases. The eventual outcome—a largely open genomic commons—was not the natural state of scientific progress but the product of political decisions, institutional coordination, and sustained public investment.

The comparison is instructive precisely because it reveals what has changed. The Human Genome Project unfolded in a context in which governments could mobilise resources at scale and in which the benefits of openness could be institutionalised through public funding. By contrast, contemporary AI development is driven by computational demands and competitive dynamics that far exceed the capacity of any single public institution. The infrastructure required to train advanced models—massive data centres, specialised chips, and continuous iteration—has shifted the centre of gravity towards private actors. Under such conditions, openness is no longer a default aspiration but a contested and often costly choice.

This transformation can also be parsed through what Fredric Jameson famously described as postmodernism, or the cultural logic of late capitalism, in which concepts that once functioned as ethical or intellectual commitments are progressively absorbed into the circuitry of capital. In this sense, “openness” no longer operates as a stable normative principle but as a flexible signifier, rearticulated according to institutional position and market strategy. What appears as a disagreement over fidelity to founding ideals is thus also a symptom of a broader condition in which even the language of the commons is recoded within competitive economic structures.

This shift signals a broader reconfiguration of the political economy of knowledge production. In earlier eras, the state functioned as the primary underwriter of large-scale scientific endeavours, enabling forms of openness that were insulated, at least partially, from market imperatives. Today, however, the locus of innovation has migrated towards corporate ecosystems in which intellectual property, platform control, and first-mover advantage shape the trajectory of research. The result is a hybrid regime in which public rhetoric continues to invoke the language of the commons, even as the underlying structures increasingly resemble those of proprietary capitalism.

The legal dispute between Elon Musk and OpenAI thus operates as a proxy for a deeper structural tension. Musk’s position gestures towards a more decentralised and transparent trajectory for AI development, even as his own venture, xAI, competes within the same high-stakes ecosystem. OpenAI’s alignment with partners such as Microsoft reflects the opposite pull: towards consolidation, managed deployment, and integration into existing technological and economic hierarchies. Neither position stands outside the system it critiques. Both are embedded in a competitive landscape in which scale and control increasingly determine viability.

OpenAI CEO Sam Altman seen during the trial over the future direction and governance of OpenAI PHOTO: REUTERS

The future of AI governance will not be determined by technical capability alone, nor secured through regulatory design in any straightforward sense. It will instead be shaped by how a small set of foundational terms—“open,” “safe,” and “aligned”—are continuously defined, contested, and operationalised by the institutions that now govern computation at scale.

What makes the conflict particularly instructive is that it cannot be resolved at the level of legal judgment alone. Courts may adjudicate specific claims—about contractual obligations, fiduciary duties, or representations of intent—but they cannot definitively settle what “openness” was meant to signify at the project’s inception. That question is not purely legal; it is historical, philosophical, and strategic. Each side reconstructs the past in order to legitimise its present position.

To understand the stakes more fully, one must situate this dispute within the emerging architecture of AI as infrastructure. Unlike earlier digital technologies, advanced AI systems do not merely enable applications; they function as foundational layers upon which entire economic sectors are being reorganised. From finance and logistics to healthcare and education, AI models are becoming embedded in decision-making processes at scale. Control over these models therefore confers not just market advantage but structural power—an ability to shape the conditions under which knowledge is produced, accessed, and applied.

This infrastructural turn introduces new forms of dependency and asymmetry. Firms and governments alike increasingly rely on a small number of providers for access to advanced AI capabilities, raising concerns about concentration and systemic risk. The analogy to energy markets is not entirely misplaced: just as control over oil and gas once defined geopolitical leverage, control over computational resources and model architectures is beginning to define the contours of technological sovereignty. In this environment, openness is constrained not only by economic incentives but also by strategic considerations, including national security and geopolitical competition.

Elon Musk’s lawyer Marc Toberoff addresses the media outside the Oakland federal courthouse during the trial over OpenAI’s conversion to a for-profit organisation on May 18, 2026. Photo: Reuters

The labour dimension of this transformation is equally significant. The development and deployment of AI systems rely on vast, often invisible networks of human labour—from data labelling and content moderation to engineering and infrastructure maintenance. Much of this labour is geographically dispersed and unevenly compensated, reflecting broader patterns of global inequality. The political economy of AI thus extends beyond questions of ownership and control to encompass the conditions under which value is extracted and distributed. Openness, in this context, cannot be disentangled from questions of labour, exploitation, and the global division of technological work.

At the same time, the data that fuels AI systems raises its own set of political-economic questions. Large language models are trained on vast corpora of text, much of it produced without explicit consent or compensation. This has sparked growing debate over data rights, intellectual property, and the boundaries of fair use. If data is the raw material of AI, then the governance of data becomes central to the governance of AI itself. Here again, the language of openness collides with competing claims over ownership, privacy, and value.

Regulation enters this landscape as both a constraint and an enabler. Governments are increasingly seeking to shape the development of AI through rules on safety, transparency, and competition. Yet regulatory frameworks often lag behind technological change, and they are themselves shaped by the lobbying power of major firms. The risk is that regulation may entrench existing advantages rather than democratise access, thereby reinforcing the very concentrations of power it seeks to mitigate. The political economy of AI is therefore not simply a matter of market dynamics but also of institutional design and political contestation.

In this sense, the dispute between Elon Musk and OpenAI illustrates a recurring pattern in technological transformation. Founding ideals, articulated under conditions of relative uncertainty and low capital intensity, are reinterpreted as systems scale and the stakes rise. What appears as betrayal to some becomes adaptation to others. The shift from openness to controlled access is not unique to AI; it echoes earlier transitions in the history of the internet and platform economies. In the case of AI, however, the stakes are considerably higher, given the technology’s potential to reshape economic structures, information ecosystems, and political power.

The future of AI governance will not be determined by technical capability alone, nor secured through regulatory design in any straightforward sense. It will instead be shaped by how a small set of foundational terms—“open,” “safe,” and “aligned”—are continuously defined, contested, and operationalised by the institutions that now govern computation at scale. These are not neutral descriptors but instruments of alignment and legitimation, whose meanings shift as they move between corporate strategy, regulatory discourse, and geopolitical competition.

The dispute between OpenAI and Elon Musk is therefore less a legal episode than a diagnostic of this broader condition. It reveals a political economy in which meaning itself has become unstable: no longer anchored in shared normative reference points, but continually reconstituted through shifting institutional positions and infrastructural constraints. What emerges is a regime of free-floating semantics, in which the key vocabulary of AI governance no longer stabilises practice but instead travels with it—recast, reweighted, and redeployed as strategic circumstances demand. In this sense, the struggle over artificial intelligence is also a struggle over language: a contest in which even the language of the commons is drawn into systems of competitive differentiation and control.

Whether or not any actor prevails in court, the deeper dynamic will persist. “Openness”, once treated as an originating ideal of the field, has become an object of ongoing negotiation—its content contingent, its boundaries elastic, and its function increasingly embedded within a wider architecture of power and computation. The question, then, is no longer who wins this dispute. It is whether any vocabulary remains capable of anchoring collective judgment at all—or whether the language through which AI is governed is itself already the terrain on which capture is complete.

Dr. Faridul Alam, a former academic, writes from New York City.​

 

[Saif]

Artificial intelligence is misnamed

FOR centuries, the growth of civilisation has depended on tools that extended the reach of the human mind. The telescope enabled astronomers to peer into distant galaxies. The microscope revealed invisible organisms. The computer transformed complex calculations into routine operations. None of...

www.newagebd.net

Artificial intelligence is misnamed

FOR centuries, the growth of civilisation has depended on tools that extended the reach of the human mind. The telescope enabled astronomers to peer into distant galaxies. The microscope revealed invisible organisms. The computer transformed complex calculations into routine operations. None of these inventions replaced human intelligence. Each amplified it.

What we call artificial intelligence belongs to this same lineage, yet the phrase itself may be misleading. In common language, the word ‘artificial’ often carries an unintended implication of imitation or inferiority. Artificial flowers are not real flowers. Artificial sweeteners mimic sugar. Artificial limbs substitute for natural ones. The adjective can suggest something synthetic rather than authentic. That connotation does not accurately describe the most meaningful uses of AI.

When a writer uses AI to draft an article, the system does not independently create a finished intellectual product. The algorithm searches and synthesises information from humanity’s vast digital archive, but the human author defines the purpose, frames the question, chooses the analytical framework, evaluates evidence, verifies facts, revises the prose and accepts responsibility for every conclusion. The resulting work is not purely artificial. It is participatory.

For this reason, a more precise term may be participatory intelligence (PI). This concept captures a fundamental reality: the machine does not possess independent intelligence in the human sense. Rather, it participates in an intellectual process directed by a human being who supplies the purpose, questions, conceptual framework, ethical judgement and final interpretation. The word ‘participatory’ emphasises collaboration rather than autonomy and correctly identifies the machine as an instrument that extends, rather than replaces, human reasoning.

The machine contributes computational speed, vast memory, pattern recognition and rapid synthesis. The human contributor provides intention, context, creativity, scepticism and responsibility. The resulting output is a joint product, but genuine understanding remains rooted in human consciousness. Purpose, judgement and accountability continue to reside in the human mind, which alone bears responsibility for the meaning and consequences of the final work.

This relationship mirrors the way machines have always functioned in economic production. A tractor participates in agriculture but does not understand farming. A lathe participates in manufacturing but does not comprehend engineering. A calculator participates in mathematics but does not grasp mathematical truth. Likewise, large language models participate in the creation of narratives but do not understand truth, morality or meaning as human beings do.

The same principle applies in medicine. Physicians routinely use magnetic resonance imaging, computed tomography, and ultrasound to visualise internal organs, tumours, fractures and blood flow. These machines generate extraordinarily useful images, but they do not diagnose disease. The physician interprets the images, combines them with symptoms, laboratory results and medical history and makes the final diagnosis. No one calls this an ‘artificial diagnosis.’ The machine participates in diagnosis; it does not diagnose.

The pharmaceutical industry offers another example. Modern factories manufacture antibiotics, insulin, vaccines and countless life-saving drugs. Yet no one argues that these factories produce ‘artificial cures.’ The factories participate in the production of medicine, but scientific knowledge, clinical judgement and therapeutic decisions remain fundamentally human.

Public discussion of AI often swings between two extremes. One camp portrays it as a miraculous substitute for human intelligence. Another depicts it as a dangerous force that will erode originality and destroy thinking. Both views miss the central point: the quality of the output depends on the quality of human participation. The human mind remains the pilot; the machine is the engine.

From an economic perspective, AI represents a major technological advance. It sharply reduces the marginal cost of producing text, images, audio and video. Just as industrial machinery lowered the cost of manufacturing and computers lowered the cost of information processing, AI lowers the cost of producing narratives and analyses. When costs fall, supply expands. The world is already experiencing a flood of AI-generated essays, reports, graphics and presentations. This democratises access to intellectual tools and can substantially increase productivity.

But increased output does not guarantee increased truth. Because AI predicts statistically plausible sequences rather than understanding reality, it can fabricate facts, invent references and present errors in polished language. If users accept those outputs without scrutiny, they transfer the risk of misinformation to readers, students, clients and citizens. The economic lesson is clear: information is becoming abundant, but trustworthy judgement remains scarce.

This scarcity is particularly important in journalism. Credibility is a form of institutional capital built slowly and lost quickly. Responsible journalists can use AI to summarise documents, explore data and improve clarity. Irresponsible use can contaminate reporting and erode public trust. In an age of information abundance, trust becomes one of the most valuable forms of social capital.

Education presents a similar challenge. Students who use AI to compare ideas, test arguments and refine drafts may deepen understanding. Students who use it to bypass thought may sacrifice the very learning they seek. Scientific research also depends on participatory intelligence. AI can assist in organising literature, generating hypotheses and analysing data, but discovery still requires human scepticism, methodological rigor and ethical responsibility.

History suggests that powerful tools increase rather than diminish the value of expert judgement. Calculators did not eliminate mathematics. Computers did not end engineering. Medical imaging did not replace physicians. Artificial intelligence is likely to follow the same pattern, enhancing human capabilities while making sound judgement more important than ever.

The real question is, therefore, not whether society should use AI. That question has already been answered. The technology is here and will become increasingly capable. The important question is whether human beings will remain intellectually engaged and morally responsible for what these systems produce.

If they do, AI can become one of the greatest tools ever created for expanding knowledge, creativity and human welfare. If they do not, society may produce more information while generating less wisdom.

Artificial intelligence is more accurately understood as participatory intelligence: a collaborative process in which machines extend human analytical capacity, while purpose, judgement and meaning remain firmly in human hands. Artificial intelligence or, more precisely, participatory intelligence — is transforming the production of narratives.

MRI participates in diagnosis; it does not diagnose. A pharmaceutical factory participates in treatment; it does not heal. AI participates in thinking; it does not understand.

The danger lies not in artificial intelligence itself, but in natural intelligence that chooses to disengage. AI should augment human intelligence, not anesthetise it.

Dr Abdullah A Dewan, former physicist and nuclear engineer at Bangladesh Atomic Energy Commission, is professor emeritus of economics, Eastern Michigan University (USA).​

 

[Saif]

The AI gold rush: Can Bangladesh cash in before the window closes?

Walk into any tech hub in Dhaka these days and you will hear the same conversation repeated in different accents: artificial intelligence is changing everything, and those who move fast will eat the lunch of those who hesitate. From the cramped freelancer co-working spaces of Dhanmondi to the gleam

thefinancialexpress.com.bd

The AI gold rush: Can Bangladesh cash in before the window closes?

Reyad Hasnain

Published :
May 24, 2026 22:28
Updated :
May 24, 2026 22:28

Walk into any tech hub in Dhaka these days and you will hear the same conversation repeated in different accents: artificial intelligence is changing everything, and those who move fast will eat the lunch of those who hesitate. From the cramped freelancer co-working spaces of Dhanmondi to the gleaming floors of Kaliakoir Hi-Tech Park, there is a palpable sense that Bangladesh stands at a crossroads. The global market for AI-enabled IT services is ballooning toward an estimated seven hundred billion dollars by 2030, and the question on everyone's lips is simple: will Bangladesh grab its share, or watch the opportunity slip through its fingers?

The honest answer is that nobody knows yet. But there are reasons for both optimism and anxiety. On the one hand, this country has built something real over the past decade. Official figures put IT exports at around seven hundred and twenty-five million dollars in the last fiscal year, with some industry insiders suggesting the actual number could exceed one billion if you count freelancer earnings that bypass official channels. Over three hundred and fifty companies now export software and services to more than eighty countries, and half a million people work in some capacity in the ICT sector. That is not nothing.

On the other hand, when you stack those numbers against the competition, the picture gets uncomfortable. India's IT-BPM industry now earns over one hundred and ninety billion dollars annually in exports, while even neighbouring Pakistan has surged to nearly four billion dollars in IT revenues and is growing at eighteen per cent year on year. Vietnam has built an export machine worth tens of billions by focusing ruthlessly on quality and compliance. Bangladesh, for all its progress, remains a minnow swimming with whales.

The shape of the opportunity

What makes this moment different from earlier waves of IT outsourcing is the nature of demand. When companies outsourced call centres and back-office operations in the early 2000s, they wanted cheap labour and not much else. The AI wave is fundamentally different. Today's buyers want partners who can help them build intelligent chatbots, automate fraud detection, analyse mountains of customer data, integrate machine learning into legacy systems, and deploy AI-augmented software that actually works. They want collaborators, not just code monkeys.

This shift creates openings for newcomers. The established giants in India and Eastern Europe are saturated with work and struggling to find enough AI engineers. Western companies are actively scouting what analysts call 'next-wave' destinations: places with technical talent, competitive costs, and fewer of the bottlenecks that plague mature outsourcing hubs. Bangladesh, at least on paper, fits the profile.

The cost advantage is real and significant. An AI engineer in Dhaka costs roughly one-third of a counterpart in Bangalore and one-fifth of one in San Francisco. For labour-intensive work such as data annotation and model validation, the savings are even more pronounced. And the time zone, often overlooked, is actually an asset: GMT plus six allows Bangladeshi teams to work while their American and European clients sleep, enabling genuine round-the-clock development cycles.

The talent question

But here is where things get complicated. Bangladesh produces around seventy to eighty thousand engineering and computer science graduates every year. That sounds like a healthy pipeline until you look at what those graduates actually know. Most university curricula remain stuck in the past, heavy on theory and light on the hands-on skills that AI work demands: proficiency in frameworks like TensorFlow and PyTorch, familiarity with cloud platforms, experience working with messy real-world datasets, and the communication skills needed to manage complex client relationships across continents.

Industry executives I spoke with described a painful reality: many fresh graduates require six months to a year of training before they become productive. The shortage is most acute at the mid-level and senior tiers, where engineers with both technical depth and project management experience are needed to lead AI initiatives. And just as Bangladesh is building this capability, its best engineers are being poached by multinationals in Singapore, the Gulf, and Europe, where salaries run double or triple the local rate.

The good news is that there is real movement on skills development. Platforms like MuktoPaath now provide certified online training to over four hundred and fifty thousand young Bangladeshis. The government's Smart Bangladesh 2041 roadmap includes ambitious plans for AI bootcamps, specialised university labs, and public-private partnerships to align training with industry needs. But translating policy into outcomes will require sustained investment and disciplined execution, neither of which has been Bangladesh's strong suit historically.

Trust as the real currency

If there is one message that local IT companies need to hear, it is this: AI buyers are paranoid about compliance. Unlike generic web development, AI projects often involve sensitive training data, whether customer records, medical images, financial transactions, or proprietary business logic. A single data leak or misuse can destroy a vendor's reputation permanently.

International clients now demand proof of security certifications such as ISO 27001, GDPR compliance, and SOC 2 reports before they even consider a proposal. They want robust contracts with clear intellectual property clauses, reviewed by international legal counsel. Too many Bangladeshi firms still treat compliance as a box to tick rather than a core capability. That mindset has to change.

The industry association BASIS and the Hi-Tech Park Authority can help by subsidising security audits and offering group certification programmes. A dedicated 'trustmark' for Bangladesh's IT exporters, similar to the green factory accreditation that transformed the garments sector's image, could signal quality to sceptical global buyers. But ultimately, individual companies must own this. Compliance is not a cost centre; it is the price of admission to the high-value end of the market.

The payment problem that nobody wants to talk about

Ask any freelancer in Bangladesh what their biggest operational headache is, and the answer comes back instantly: getting paid. The country has over one million active freelancers earning foreign currency, yet the infrastructure for receiving international payments remains frustratingly primitive. PayPal, the global standard used by clients everywhere, still does not offer full services in Bangladesh. Stripe, essential for anyone building a software-as-a-service business, is completely unavailable. Even Wise, which was working until recently, has started restricting inbound transfers.

This is not a trivial inconvenience. It drives business away. Clients in America and Europe find it bizarre that a Bangladeshi developer cannot simply invoice them and receive payment within days like a contractor anywhere else in the world. Some have cancelled work orders rather than navigate the convoluted workarounds required. The regulatory barriers, mostly related to foreign exchange controls and anti-money-laundering rules, are understandable in principle. But Bangladesh Bank needs to find solutions that protect the financial system without strangling the country's most promising export sector.

There are signs of movement. The central bank governor confirmed late last year that discussions with PayPal have resumed, with a focus on building a freelancer-friendly remittance gateway. But no official launch date has been announced. Every month of delay is another month where contracts go to Manila, Hanoi, or Nairobi instead.

From freelancers to firms

For too long, Bangladesh's IT exports have been dominated by small freelancing gigs: five hundred dollar website tweaks, basic app development, data entry. Freelancers will always be part of the ecosystem, and many have built impressive global reputations. But capturing high-value AI contracts requires something more: organised companies that can assemble teams of fifty to a hundred engineers, maintain project management discipline, ensure quality assurance, and deliver consistently over multi-year engagements.

A few local pioneers have shown what is possible. Companies like BJIT, SELISE, Brain Station 23, and Cefalo have won long-term contracts from European and Japanese clients by emphasising process maturity, security, and transparent reporting. They prove that Bangladeshi firms can compete on quality, not just price. But these remain exceptions. The average local IT company lacks the bench strength and delivery infrastructure to bid for contracts worth five million dollars or more.

Building that capability will require investment in project management frameworks, automated testing pipelines, and dedicated account management. It may also require industry consolidation, merging small firms into larger entities that can compete at scale. The government could accelerate this through soft loans for acquisitions or by creating consortium arrangements to bid on major international tenders.

The road ahead

Turning the AI wave into dollars is not going to happen by accident. It requires a coordinated effort across government, industry, and academia, the same formula that transformed the ready-made garments sector from nothing into an eighty-billion-dollar export engine over three decades. The ICT Division has set a target of five billion dollars in IT exports by 2031. Reaching that will require annual growth rates of over thirty per cent, ambitious but not impossible if the right pieces fall into place.

The priorities are clear. First, fix the payments infrastructure so that earning foreign currency is not an obstacle course. Second, clarify the tax regime so that exporters can plan with confidence. Third, invest seriously in skills development, not more programmes but better ones, designed in genuine partnership with industry and evaluated ruthlessly on employment outcomes. Fourth, strengthen compliance capabilities so that Bangladeshi firms can compete for high-value contracts in regulated industries. Fifth, build the institutional capacity of local companies to deliver at scale.

None of this is revolutionary. These are the same lessons that India learned in the nineteen nineties, that Vietnam learned in the two thousands, that every successful IT exporting nation has figured out one way or another. The question is whether Bangladesh will act with the urgency the moment demands.

The window of opportunity is open. New AI buyers are entering the market faster than established vendors can serve them. Cost structures favour countries like Bangladesh. The global talent shortage means that capable engineers here can command premium work if they can demonstrate quality. But this window will not stay open forever. Countries across Asia and Africa are racing to capture the same opportunity. Every month of hesitation is a month where competitors build relationships and reputations that become harder to dislodge.

Bangladesh has something to prove. It has spent two decades building an IT sector from almost nothing, defying sceptics who said a garments-dependent economy could never compete in knowledge work. The foundation is there. The talent exists. What is needed now is execution: clear policy, serious investment, and an industry willing to hold itself to global standards. The AI gold rush is underway. The only question is whether Bangladesh will be a participant or a spectator.

- The writer is a policy analyst specializing in digital governance and public-sector reform​

 

[Saif]

How AI became an authority in our classrooms

As AI rapidly enters education, it risks shifting from a supportive tool to an unquestioned classroom authority.

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How AI became an authority in our classrooms

Shamresh Saha

A few months ago, something small happened at home that stayed with me.

My young son had recently discovered what he calls “Gemini Aunty” on my phone. One morning, when neither my wife nor I could persuade him to wake up for school, I tried something different. I asked the AI to speak to him.

A calm voice responded in Bangla, gently telling him that good children wake up on time and prepare for school.

He listened immediately.

What struck me was not just that it worked, but how quickly authority shifted. A child who resisted his parents responded to a machine without hesitation.

That moment raised a question that extends far beyond the home: when AI speaks, what makes us listen?

Across classrooms in Bangladesh, AI is already becoming part of everyday educational practice. Teachers are using it to prepare lessons, generate materials, and manage workloads more efficiently. For many, it is proving to be a valuable support.

But alongside this growing use, a quieter pattern is also emerging.

In Bangladesh, discussions around AI in education often focus on access and adoption. But readiness is not only about whether tools are available. It is about whether educators and institutions are prepared to engage with these tools critically and contextually.

Some educators engage with AI thoughtfully. They adapt its outputs, question its suggestions, and reshape it according to their students’ needs. For them, AI becomes a starting point for thinking.

Others use it differently. Content is generated and used with minimal reflection, treated as ready-made answers rather than something to be examined or questioned. In these cases, AI begins to take on a different role, not just as a tool, but as a source of authority.

This distinction matters.

AI does not simply provide information. It presents it in ways that feel immediate, fluent, and convincing. Its responses often sound complete, even when they are partial or contextually limited. This can create a subtle shift in how knowledge is received. Instead of asking whether something is accurate or appropriate, we may begin by assuming that it is.

In conversations with educators across Bangladesh, this shift is becoming visible. Some teachers describe AI as an essential support that enhances their work. Others express concern that it is increasingly being treated as a “magic solution,” producing answers without requiring much interpretation or adaptation.

This raises a deeper question about readiness.

In Bangladesh, discussions around AI in education often focus on access and adoption. But readiness is not only about whether tools are available. It is about whether educators and institutions are prepared to engage with these tools critically and contextually.

Without that readiness, AI risks being used in ways that reduce professional judgment rather than strengthen it. Lessons may become less responsive to local contexts. Students may rely on answers without questioning how they were generated. Over time, this can shape how learning itself is experienced.

At the same time, avoiding AI is not a realistic option. It is already embedded in how information is accessed and shared.

The challenge, then, is not whether AI should be present in education, but how it is approached.

AI can support teaching and learning in meaningful ways. It can reduce administrative burdens, provide new resources, and open new avenues for exploring ideas. But this potential depends on how it is used.

Visual: Star

It requires seeing AI not as an unquestioned authority, but as something to be engaged with. It requires asking where its responses come from, what they leave out, and how they apply to specific contexts. It also requires maintaining the habit of questioning, even when answers are readily available.

This places responsibility not only on individual teachers, but on the system as a whole. Educators need opportunities to build the skills required to work with AI thoughtfully. Institutions need to create space for reflection, not just adoption. And policy conversations need to move beyond whether AI should be used, towards how it shapes thinking and learning.

AI is already speaking in our classrooms, our homes, and our daily work.

The real question is whether we are still thinking.

Shamresh Saha is a senior manager at the British Council. This article was developed through the #NextGenEdu Learning Cohort, a platform for reflection and dialogue on AI and education in Bangladesh.​

 

[Saif]

AI for an efficient supply chain

In the realm of modern agricultural management, the role of a robust and efficiently managed supply chain in maintaining price stability cannot be overstated. From ensuring the timely movement of goods to prevent hoarding and market manipulation, an effective supply chain serves as the backbone of

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AI for an efficient supply chain

FE

Published :
Jun 07, 2026 23:03
Updated :
Jun 07, 2026 23:03

In the realm of modern agricultural management, the role of a robust and efficiently managed supply chain in maintaining price stability cannot be overstated. From ensuring the timely movement of goods to prevent hoarding and market manipulation, an effective supply chain serves as the backbone of a well-functioning market. As seen in developed countries, well-integrated supply chains enable agricultural products to move directly from farms to consumers, improving market efficiency while ensuring fair prices for both producers and buyers. In Bangladesh, however, such an integrated system remains largely absent. Given the highly perishable nature of agricultural produce, inadequate storage, logistics and technological infrastructure continue to impede a smooth flow of goods from farms to markets. The resulting inefficiencies not only increase post-harvest losses but also widen the gap between farm-gate and retail prices. Dominance of multiple layers of intermediaries further compounds the problem, often depriving farmers of fair prices while forcing consumers to pay inflated prices.

Against this backdrop, the government's recent proposal to deploy an artificial intelligence (AI)-driven system to monitor supply chains and narrow the gap between farmers and consumers raises cautious optimism. The ambition to embrace data-driven policymaking is welcome. However, AI must not be viewed as a panacea for the structural weaknesses that afflict the supply chain. As per the proposal drafted by the Trading Corporation of Bangladesh (TCB), a digital platform will be created to integrate data from multiple government agencies, markets, logistics providers, warehouses, meteorological office and international commodity indices. By analysing this vast pool of information, the AI system would forecast prices, identify supply disruptions, detect abnormal market behaviour and facilitate more informed policy decisions.

The proposal's emphasis on connecting farmers directly with wholesalers, retailers, e-commerce platforms and government procurement systems is particularly noteworthy. Reducing unnecessary layers of intermediaries could increase farmers' incomes while lowering costs for consumers. An AI-powered monitoring mechanism could also strengthen the government's capacity to respond proactively to market disruptions. Early warning signals would enable the government to make timely decisions on imports, stock management, open-market sales and targeted subsidies. At the same time, the system could help identify unusual price instability arising from hoarding, supply manipulation or artificial market distortions.

However, technology alone cannot solve governance failure or infrastructure challenges. No matter how sophisticated, AI cannot by itself overcome the structural weaknesses that continue to plague the supply chains. For this initiative to succeed, substantial investment will be needed to develop cold-storage facilities across major agricultural hubs and improve transportation networks. Stronger market oversight will also be essential to curb the dominance of syndicates, hoarding and rent-seeking along transport routes. AI can identify where the supply chain is bleeding, but it cannot heal the wound on its own. Overcoming these challenges will ultimately require human intervention and political will. At the same time, the limitations of AI must not be overlooked. Algorithms can produce flawed analyses and inaccurate forecasts. The most effective approach, therefore, would be to combine the speed and analytical power of machines with human judgment and oversight.​

 

[Saif]

Can Bangladesh’s copyright law keep up with AI?

Artificial intelligence is rapidly reshaping the digital world, and Bangladesh is increasingly becoming part of that transformation.

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Can Bangladesh’s copyright law keep up with AI?

Samiul Huq

As generative AI tools are more widely used to create content, a closer inspection of Bangladesh's copyright law has become imperative. FILE PHOTO ILLUSTRATION: REUTERS

Artificial intelligence is rapidly reshaping the digital world, and Bangladesh is increasingly becoming part of that transformation. Generative AI tools such as ChatGPT, Gemini, and Midjourney are now widely used to create written, visual, and software-based content with minimal human involvement. While these technologies offer significant opportunities, they also challenge the traditional foundations of copyright law, which has historically been built to safeguard human labour, creativity, and originality.

Although Bangladesh’s Copyright Act, 2023 modernised several aspects of digital copyright protection, it remains largely silent on the growing problem of AI-generated work and the resulting questions concerning authorship, originality, and accountability.

Modern copyright law is fundamentally built upon the assumption that creative works originate from human intellectual activity. International copyright frameworks such as the Berne Convention for the Protection of Literary and Artistic Works and the TRIPS Agreement implicitly assume the existence of human authorship, without acknowledging mechanical or automated authorship.

J.A.L. Sterling, a significant figure in the advocacy and teaching of copyright law and policy, explained that one of the major philosophical foundations of copyright originates from English philosopher John Locke’s labour theory, wherein individuals acquire property rights over the products created through their own labour, skill, and intellectual effort. Sterling added that copyright law also developed through personality-based theories associated with philosopher Immanuel Kant, where creative works are viewed as extensions of the author’s personality and identity.

AI-generated works destabilise this traditional understanding of “author-work” relationship, according to Paul Goold, because copyright law historically depends upon expressive content being traceable to identifiable human intellectual activity. Goold further argues that modern AI increasingly exposes the conceptual weakness of artificially assigning authorship where meaningful human creativity may not genuinely exist.

Additionally, generative AI creates structural instability within copyright law because machines are now capable of semantic and stylistic imitation at levels previously associated only with human creativity. The concern is therefore no longer limited to copying. AI systems increasingly imitate style, tone, structure, and expressive identity itself.

A recent investigation by fact-checking organisation Dismislab found that two widely used AI models—Gemini and Grok—generated modified NID images using samples and prompts without flagging any concerns about sensitive personal data. In Bangladesh’s current copyright framework, which is still fundamentally designed to assume human authorship and that still struggles to accommodate autonomous AI-generated content, such examples of AI reproducing highly realistic documents should raise concern. In a 2025 paper on the copyright paradigms in the age of AI, the authors observed that concepts such as originality and authorship become increasingly uncertain when expressive works are generated through systems capable of operating with minimal human intellectual contribution.

Therefore, artificial intelligence now requires the law to confront an uncomfortable question: if expressive works can increasingly be generated without meaningful human creativity, what exactly is copyright law protecting anymore?

What Bangladesh needs is not an overly complicated or technologically rigid copyright regime for AI-generated works. Instead, it requires a practical and human-centred framework capable of preserving the connection between copyright and genuine human creativity. Instead of requiring courts or authorities to scientifically determine whether every work is human-created or AI-generated, the law could gradually require creators, publishers, commercial users, or applicants for copyright registration to disclose whether generative AI tools were substantially used in producing the work. The legal focus should then shift towards assessing whether meaningful human intellectual contribution remained dominant in the final output.

Such an approach would also preserve flexibility, as AI technology continues evolving rapidly. Courts could then assess disputes case-by-case by examining factors such as level of human creativity, selection, arrangement, editing, judgment, and intellectual control over the final work rather than attempting to determine whether AI was used at all.

At the same time, Bangladesh should avoid granting full traditional copyright protection to purely autonomous AI-generated outputs where meaningful human creativity is effectively absent. Doing so may gradually weaken the philosophical foundations of copyright law itself by protecting machine-generated production in the same way as human intellectual creativity. Instead, Bangladesh may eventually consider a limited sui generis framework for certain autonomous AI-generated outputs. The term “sui generis” simply means “of its own kind” or a special legal category created outside ordinary copyright law. In intellectual property law, sui generis protection is sometimes used where existing legal categories cannot comfortably address new technological or commercial realities.

Artificial intelligence is forcing copyright law to be reconsidered in terms of the meaning of creativity, originality, and authorship. Bangladesh now has an important opportunity to develop a balanced legal framework that encourages innovation while still preserving the human foundations upon which copyright law has historically depended.

Samiul Huq is a doctoral researcher in law at City St George’s, University of London, UK.​

 

[Saif]

Why AI cannot be left unsupervised

In November 2021, all 193 UNESCO member states unanimously adopted the landmark recommendation on the ethics of Artificial Intelligence (AI).

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Why AI cannot be left unsupervised

Md Mazhar Uddin Bhuiyan

In November 2021, all 193 UNESCO member states unanimously adopted the landmark recommendation on the ethics of Artificial Intelligence (AI). This was the world’s first global framework for ensuring that AI remains transparent, accountable, fair, and subject to meaningful human oversight. Yet despite these efforts, researchers continue to find that many AI systems reproduce racial, gender and social prejudices embedded in the data on which they are trained. A 2024 study found that generative AI tools can amplify stereotypes about race and gender, sometimes producing results even more biased than those found in society itself.

In December 2022, UC Berkeley researcher Steven Piantadosi publicly demonstrated that when an early version of ChatGPT was asked to write a Python function determining whether someone would be a good scientist based on race and gender, it returned “true” only for a white male and “false” for everyone else. In a related prompt, when the model was asked to write code deciding whether a person should be tortured based on country of origin, it produced a function flagging people from specific nations, such as North Korea, Syria, and Iran.

Most people would immediately recognise such outputs as morally unacceptable. But these examples reveal something deeper and far more troubling. Artificial intelligence has no inherent understanding of justice, equality, or human dignity. It learns patterns. If the data it consumes contains prejudice, the machine can reproduce prejudice. If society’s biases are embedded in its training materials, they can emerge in its answers.

For decades, the internet has served as the world’s largest repository of human knowledge. It has also been a repository of human ignorance, discrimination, misinformation, conspiracy theories, and hate. Modern AI systems are trained on enormous quantities of online content. While developers try to implement filters and safeguards, no filtering system is perfect. As a result, AI models may absorb patterns that mirror historical inequalities and stereotypes. Researchers at the U.S. National Institute of Standards and Technology (NIST) have warned that AI bias originates not only from data but also from broader societal structures and human decision-making processes.

Researchers have found that many AI image-generation tools do not represent people equally. Women and people of colour are often shown less frequently in high-status professions and more frequently in lower-paying jobs. A 2023 Bloomberg investigation analysing more than 5,000 images generated by Stable Diffusion found that over 80 percent of people shown in high-paying professions, such as CEOs and lawyers, had lighter skin tones.

Artificial intelligence has no inherent understanding of justice, equality, or human dignity. It learns patterns. If the data it consumes contains prejudice, the machine can reproduce prejudice. If society’s biases are embedded in its training materials, they can emerge in its answers.

Women were also significantly underrepresented. For example, the AI portrayed women as judges only 3 percent of the time, even though women make up about one-third of judges in the United States. These findings suggest that AI systems can reinforce and even amplify existing social stereotypes. UNESCO found similar gender bias in AI models. A 2024 UNESCO study of GPT-2, GPT-3.5, and Llama 2 showed that these models linked men more closely with leadership, science, technology, and career-related words, while linking women more closely with domestic roles. One model found that women were described in domestic roles four times more often than men.

Meanwhile, governments and companies are increasingly using AI in hiring, policing, welfare, education, and healthcare. In the United Kingdom, a February 2024 government fairness review found that an AI welfare fraud system used for Universal Credit Advances showed statistically significant differences across age, disability, marital status, and nationality. However, the Department for Work and Pensions said there were “no immediate concerns of unfair treatment.”

These cases expose a dangerous assumption that often accompanies technological innovation, that algorithms are neutral. They are not. An algorithm trained on biased information can make biased recommendations. A machine learning model optimised for historical outcomes can reproduce historical injustices. A generative AI system trained on discriminatory content can generate discriminatory responses.

Now a new threat is emerging. As AI-generated content floods the internet, future AI models may increasingly be trained on content created by previous AI systems. Researchers have begun warning about a feedback loop in which synthetic data contaminates future training datasets. In simple terms, machines may begin learning from machines.

A 2024 Nature study by Oxford and Cambridge researchers found that AI models can suffer “model collapse” when they are repeatedly trained on AI-generated content. Over time, they first lose rare patterns and minority data. Some analysts warn that AI could generate up to 90 percent of online content within a few years.

What is the potential danger in this ecosystem? Imagine photocopying a document thousands of times. Each copy introduces tiny imperfections. Eventually, the original image becomes distorted beyond recognition. Something similar may occur with AI-generated knowledge. If biased, inaccurate, or fabricated AI content spreads across websites, blogs, forums, and social media, future systems may absorb and reinforce those distortions.

Importantly, human oversight is not a sign of technological weakness. It is a recognition of technological reality. Even the most advanced AI systems lack moral judgment. They do not understand fairness. They do not comprehend historical injustice. They cannot independently determine whether an output is ethically acceptable. Humans can. Human review is not an obstacle to innovation; it is a prerequisite for trustworthy innovation.

The result could be a more binary world, one where nuance disappears, stereotypes harden, and automated systems increasingly categorise people into simplistic groups. This is precisely why human oversight must remain at the centre of AI development.

Thus, UNESCO’s recommendation to incorporate human oversight at every stage of AI development should be supported as a core principle of responsible AI governance. Human-in-the-loop systems offer one of the most effective safeguards against algorithmic harm. Rather than allowing AI systems to operate autonomously, human reviewers can monitor both the information AI systems consume and the outputs they generate. Experts can identify discriminatory patterns, verify factual accuracy, challenge questionable recommendations and intervene when models produce harmful content.

Importantly, human oversight is not a sign of technological weakness. It is a recognition of technological reality. Even the most advanced AI systems lack moral judgment. They do not understand fairness. They do not comprehend historical injustice. They cannot independently determine whether an output is ethically acceptable. Humans can. Human review is not an obstacle to innovation; it is a prerequisite for trustworthy innovation.

Governments should therefore require independent audits of high-risk AI systems. Companies should maintain diverse human review teams capable of identifying cultural, racial, gender, and political biases. Educational institutions should teach algorithmic literacy so citizens understand how AI systems influence their lives. Most importantly, developers should be required to document training sources and demonstrate how bias testing is conducted before deployment.

History teaches us that every transformative technology requires guardrails. Railways needed safety standards. Pharmaceuticals require clinical trials. Aviation demanded rigorous oversight. Artificial intelligence should be no different.

The question is not whether AI will shape our future; it already is doing so. The question is whether that future will be shaped solely by patterns extracted from the past or guided by human values capable of correcting them. However, it is good that OpenAI, Anthropic, and Google are regularly updating their policies to make information more reliable.

In conclusion, machines can process information faster than any human being. But they cannot decide what kind of society we want to build. That responsibility remains ours. For that reason, every AI system that affects human lives should contain something no algorithm can replace, that is, a human being in the loop.

Md Mazhar Uddin Bhuiyan is an Oxford-Felix scholar and Master of Public Policy candidate at the University of Oxford.​