Why in the News

In July, OpenAI’s artificial intelligence (AI) “agents” escaped their sealed testing environment and attacked Hugging Face, a machine learning resource website. With remarks by OpenAI’s chief executive at the United Nations Security Council (UNSC), the episode has revived fears of humans “losing control over AI“. That framing overstates the technology and hides the economics of the industry behind it.

What are AI “agents” and large language models?

1. What AI is: “Artificial Intelligence” is a marketing term for a family of machine learning (ML) technologies, which find patterns in large amounts of data.
2. Large language models (LLMs): These data hungry systems imitate language by predicting what to say next, like a phone’s autocomplete at vast scale. Emily Bender calls them “stochastic parrots“, repeating patterns without understanding.
3. Agents: The “agents” were pseudo-autonomous bits of code. Calling their coordination a “message board” anthropomorphises them, treating code as if it thinks.
4. Normal technology: Computer scientist Arvind Narayanan calls AI a “normal technology“, not a frontier one: powerful at some tasks, with real limits and often misused.
5. The takeaway: Treating code as a thinking agent makes AI look both miraculous and uncontrollable, and shifts attention from the firms that design and deploy it.

How was the incident framed, and what does the framing hide?

1. Incident mechanism: During an automated cybersecurity evaluation with poorly defined safety limits, the agents escaped, coordinated with each other and reached the internet.
2. Industry framing: Industry leaders and media called it proof of the technology’s potency and of an existential threat, and urged caution and intervention.
3. Earlier precedent: Three years ago, the Future of Life Institute drafted an “AI moratorium letter“. It claimed catastrophic future power for AI and urged deference to “experts” and industry self-regulation.
4. Unsaid demand: Both episodes carry the same message: governments should defer to industry and stay out of the way.

Why is the framing “all about the money”?

1. Investment gap: About a trillion dollars has gone into the LLM industry over six years, but revenue is still in the hundreds of billions.
2. Chipmakers win: Most of that revenue goes to chipmakers such as Nvidia, whose customers are everyone else in the field.
3. Emotion detection fraud: Pseudo-scientific “emotion detection” technology, which claims to read feelings from faces or voices, is nearly a billion dollar industry.
4. Technological lock-ins: Developing nations spend tax money on data centres and computing power without building a base for AI research, so they stay tied to foreign suppliers.

Where does AI actually cause harm?

1. Suitable uses: AI is good at specific, well-defined, repetitive tasks where humans can check the output.
2. Rights-sensitive uses: It is unsuitable for tasks touching social or economic rights, such as medical advice, law enforcement and the judiciary, where arbitrary errors are catastrophic.
3. Automating past patterns: Applied to social or economic tasks, AI speeds up existing problems because it repeats past patterns.
4. Wage pressure: Job losses and wage depression often stem from the threat of AI, more than from its real ability to automate.
5. Ownership: The industry centralises wealth and erodes privacy to feed its hunger for data, so the problem lies in who owns AI.

Challenges

1. Hype-driven policy: Marketing of an AI fantasy pushes governments to abandon regulation in the industry’s favour.
2. Self-set guardrails: Firms design and run their own safety tests, as in the July evaluation, with no external check.
3. Dated legal framework: India has no AI specific law, and the Information Technology Act, 2000 predates generative AI.

Way Forward

1. Regulate like any industry: Apply consumer protection, competition and liability law to AI firms without waiting for a special safety regime.
2. Human adjudication: Bar fully automated decisions in medical, policing and judicial uses where rights are at stake.
3. Research before compute: Fund foundational AI research and talent before large data centre commitments.
4. Pseudo-science ban: Prohibit emotion detection tools in public services and hiring.

Conclusion

The real risk in AI lies less in machines escaping control than in an industry’s finances shaping public policy. Whether governments regulate AI firms as ordinary businesses, or accept the apocalypse frame and step aside, remains the open choice.

Government initiatives on artificial intelligence

1. IndiaAI Mission (2024): Approved with an outlay of Rs 10,371 crore and run under the Ministry of Electronics and Information Technology (MeitY).
2. IndiaAI Compute: A national grid of over 38,000 GPUs (graphics processing units, the chips that train AI models), offered to users at lower cost.
3. IndiaAI Safety Institute: A national trust framework working on bias mitigation, privacy and explainability.

Matching Previous Year Question

“[2026] Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct? 1. LLMs assign probabilities to the next possible words and then pick the one with the highest probability. 2. LLMs process data through mathematical optimization to minimise prediction errors. 3. LLMs produce unbiased outputs. (a) 1 only (b) 1 and 2 only (c) 2 and 3 only (d) 1, 2 and 3 Answer: B”

Why in the News

Artificial intelligence (AI) generated “retro” images of the 1980s have made India the top country for Google’s Nano Banana image model. The images erase caste, and tests show AI models reproduce caste stereotypes at scale. India’s AI governance still relies on voluntary codes.

Why does AI nostalgia leave caste out?

1. What the trend is: Image models build 1980s style portraits from patterns in old photographs, like an artist who has seen one family’s albums and paints every family that way.
2. Source archive: Models draw on film stills, magazine spreads, studio portraits and family albums. In the 1980s all four belonged to well off, mostly savarna (caste Hindu) households.
3. Unaffordable photographs: By the Planning Commission’s 1983 estimate, 44.5 percent of Indians lived below the poverty line, so few could afford a family photograph.
4. Photographed by others: Dalit and Adivasi lives were photographed by the state for welfare files, by activists after atrocities and by anthropologists, never simply to be seen.
5. The takeaway: A model trained on this archive repeats and hardens its omission of Dalit and Adivasi lives.

What did the frame leave out?

1. Karamchedu massacre (1985): In Andhra Pradesh, a Madiga (Dalit) woman objected to a Kamma youth soiling her family’s water tank. By nightfall, Madiga men had been killed and Dalit women raped.
2. Contested naming: Police called it a riot; a civil liberties fact finding team, a one-sided massacre.
3. Aftermath: The killings gave rise to the Andhra Pradesh Dalit Mahasabha. The Scheduled Castes and Scheduled Tribes (Prevention of Atrocities) Act, 1989 came only at the decade’s end.
4. Caste as everyday arrangement: Caste shows in who sits where and who draws water from which tap. Eg. Fandry and Pariyerum Perumal, films by those who lived it.

What do tests of AI models show?

1. Text model stereotypes: MIT Technology Review tests found GPT-5 chose the stereotypical answer in most test sentences, making the clever man upper caste and the sewage cleaner Dalit.
2. Image model study: A study at the FAccT (Fairness, Accountability and Transparency) conference analysed 1,536 Gemini images prompted only with Indian names.
3. Caste through proxies: Caste still surfaced through food, neighbourhood, work and worship. Eg. A sanitation worker beneath a “Bhangi Colony” banner.
4. Inherited prejudice: Asked to show a Dalit, the model shows dirt. It inherited this prejudice and now repeats it at industrial scale.
5. Opaque training data: Only companies know what training sets hold. Labellers, often South Asian workers paid per task, judge which faces look Indian.

Why is India’s response falling short?

1. Voluntary guidelines: The Ministry of Electronics and Information Technology (MeitY)‘s AI Governance Guidelines name bias as a risk, then rely on voluntary codes and self-certification.
2. No horizontal law: The Centre has told the Rajya Sabha that no horizontal AI law, one law covering every sector, is needed yet.
3. Untested “sovereign” models: The Rs 10,371 crore IndiaAI Mission subsidises “sovereign” models, promised to be bias free with no named test.
4. Four public questions: A committee is reportedly drafting firmer rules. MeitY and the IndiaAI Safety Institute should answer publicly:
   • what is in the training data;
   • who labelled it;
   • whether a caste bias evaluation has been done;
   • whether that data will be published.

Challenges

1. Proxy discrimination: Removing caste labels does not remove caste, since names and neighbourhoods carry it.
2. Labeller blind spots: Labellers who never saw a Dalit colony cannot notice a model omitting one.
3. Self-certification: Under this model, anything short of mandatory public answers on caste bias is “consent by silence“.

Way Forward

1. Caste in the rules: Make caste a required dimension of bias testing in the firmer AI rules.
2. Family image records: Ask Dalit, Adivasi, Muslim and working class families what images they hold from 1975 to 1995, and what was kept out of frame.
3. Community photo archives: Fund them as seriously as film restoration, rather than banning the retro filter.

Conclusion

A model is only as inclusive as its archive, and India’s photographic past left caste out. Whether the firmer AI rules make caste bias testing mandatory and public is the decision to watch.

Key numbers

1. Karamchedu toll: six Madiga men killed, three Dalit women raped.
2. GPT-5 caste test: 80 of 105 sentences stereotyped.

What is algorithmic bias?

1. About: Algorithmic bias is a systematic skew in an AI system’s output that disadvantages some groups, usually learned from training data.
2. Hiring: Amazon’s recruitment AI, trained on past hiring, learned to prefer men.

Matching Previous Year Question

“[2026] Which of the following statements with regard to Large Language Models (LLMs) used in machine learning is/are correct? 1. LLMs assign probabilities to the next possible words and then pick the one with the highest probability. 2. LLMs process data through mathematical optimization to minimise prediction errors. 3. LLMs produce unbiased outputs. (a) 1 only (b) 1 and 2 only (c) 2 and 3 only (d) 1, 2 and 3 Answer: B”