The 80s nostalgia holds caste bias

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”