AI for All or Exclusion by Default? Open Letter to PM Narendra Modi on Disability Bias in Artificial Intelligence, Accessibility Challenges, and Lessons from India AI Impact Summit 2026 – Addressing TechnoAbleism in India's AI Policy and Governance

Date: 26/02/2026

To,

Shri Narendra Modi Ji
Hon’ble Prime Minister of India
South Block, New Delhi

Subject: On Artificial Intelligence, Disability Bias, and the Meaning of “AI for All”

Hon’ble Prime Minister,

Namaskar.

I write to you not as a technologist, nor as a lawyer by formal training. I write as a citizen who lives with disability, and as someone who has had to understand both law and technology simply in order to participate in ordinary life. Much of what I know about systems has not been learnt in classrooms. It has been learnt at doorways without ramps, on websites without structure, and in digital forms that could not be completed without assistance.

Exclusion rarely announces itself. It is usually designed quietly.

At the India AI Impact Summit 2026, when your address was translated in real time through an AI-powered sign language avatar, I watched carefully. It was an impressive demonstration, certainly. But it was also something more subtle. For a brief moment, Access was visible. It was not an afterthought. It stood alongside innovation, not behind it. That visibility matters. It signals direction.

I would also like to draw to the attention of Shri Narendra Modi that Moneylife published an article entitled “TechnoAbleism in India’s AI Moment: Why Accessibility Is Not Enough” [click here to read article] on 17 February 2026, coinciding with the India AI Impact Summit’s session on disability. The piece shows that this issue is already the subject of public discussion and media scrutiny, which underlines the urgency of treating accessibility and disability bias as central elements of India’s AI programme.

Yet direction must be followed by design.

We speak today of “AI for All.” It is a powerful phrase. But if it is to carry meaning, it must confront a difficult truth: artificial intelligence systems, as they are presently trained and deployed across the world, tend to absorb and reproduce the biases already present in society. Disability is not excluded intentionally. It is excluded structurally.

Artificial intelligence learns from data. That data is drawn from the world as it has been recorded. The recorded world, especially the digital one, reflects certain assumptions about how a person moves, speaks, types, sees, processes information, and builds a career. The so-called average user becomes the reference point. Systems are optimised around that reference point. Others are accommodated only when someone remembers to ask.

In such systems, disability becomes an exception.

This becomes visible in small but telling ways. When generative AI tools are asked to create websites or applications, they often produce code that assumes mouse navigation, adequate vision, and conventional interaction patterns. Keyboard accessibility may not be complete. Structural markup for screen readers may be missing. Alternative text may not be generated unless explicitly requested. Colour contrast frequently fails established accessibility norms.

Unless instructed, accessibility does not appear by default.

That word, default, is where the real issue lies.

Under the Rights of Persons with Disabilities Act, 2016, and under India’s obligations pursuant to the United Nations Convention on the Rights of Persons with Disabilities, accessibility is not optional. It is not decorative. It is a matter of Equality and Dignity. The Hon’ble Supreme Court has affirmed that accessibility is foundational to the exercise of fundamental rights. Without access, rights remain theoretical.

When artificial intelligence begins to generate systems at scale, inaccessible design also begins to scale. What was once a single inaccessible website becomes hundreds. What was once a human oversight becomes an automated pattern. Exclusion is no longer episodic. It is multiplied.

A citizen need not be denied formally. She may simply be unable to use what has been built.

India has articulated an ambitious artificial intelligence architecture, extending from infrastructure and compute to foundational models and applications. The vision is large. The confidence is visible. But I worry about timing. If disability is considered only at the application stage, after the underlying models have already been trained on datasets that insufficiently represent disability experience, then correction later will be partial and costly.

Bias does not remain soft once embedded. It settles into systems.

We have seen, in other technological domains, a familiar cycle. Innovation is celebrated. Adoption expands rapidly. Harm becomes visible only after scale has been achieved. Regulation then attempts to repair what might have been prevented. Artificial intelligence operates at a velocity and magnitude that make delayed correction far more difficult.

The Book of Proverbs says, “Where there is no vision, the people perish.” I do not read that verse as theological warning. I read it as policy advice. Vision must mean foresight; asking who is not being seen.

Around the world, governments have begun to grapple with these questions. The European Union has enacted an Artificial Intelligence Act that links AI governance explicitly to fundamental rights and non-discrimination. High-risk systems are subject to structured assessment and documentation. Bias audits and impact assessments are becoming part of regulatory vocabulary in several jurisdictions. The conversation is no longer limited to efficiency. It includes fairness.

India, as a State Party to the UN Convention on the Rights of Persons with Disabilities, is already bound by obligations to ensure equal access to information and communication technologies. These commitments do not diminish because technology evolves. If anything, their relevance increases.

This is not an argument for importing foreign law. It is an argument for aligning our technological progress with our own constitutional morality.

There is another dimension that requires attention, and it cannot be resolved by rhetoric alone. We need structured, publicly supported research on disability bias in artificial intelligence systems. Not assumption. Not symbolic inclusion. Research.

Datasets must be examined for representational gaps. Model outputs must be tested systematically across disability-related contexts. Evaluation metrics must measure performance across diverse sensory and cognitive realities. Without such empirical work, we shall continue to debate in abstraction.

Artificial intelligence is not only engineering. It touches law, sociology, governance, ethics, and lived experience. Universities such as NALSAR and other institutions working at the intersection of law and public policy ought to collaborate with technical institutes developing AI systems. Organisations grounded in disability rights must be involved as knowledge partners, not merely consulted at the end.

Public funding is being directed towards compute capacity, innovation ecosystems, and model development. A focused allocation for research on AI and disability bias would not be disproportionate. 

Yet its impact would be long-term and structural.

The Government of India ought undertake such a structured research initiative on artificial intelligence and disability bias, I would respectfully seek to be involved in that effort. For several years, I have been examining this question in depth and have maintained a dedicated platform, thebiaspipeline.nileshingit.org [click here to visit site], where I have written extensively on disability bias in digital systems and AI. While many organisations in India are rightly focused on accessibility compliance, very few are examining algorithmic bias itself as a systemic concern. I believe my sustained work in this area positions me to contribute meaningfully to any national research initiative. Significant public resources are presently being invested in artificial intelligence. If disability bias is not studied with equal seriousness, an important dimension of inclusion risks being overlooked. The promise of “Sabka Saath, Sabka Vikas” cannot be realised if persons with disabilities are not structurally included in the design and evaluation of emerging technologies.

Over the past year, I wrote to the Ministry of Electronics and Information Technology and to NITI Aayog when national AI policy discussions were underway. My intention was simple: to place before them the structural concerns surrounding disability bias in AI systems. I have not received substantive responses. I mention this not as complaint, but as indication that this dimension has not yet been treated with the seriousness it deserves.

The phrase “human in the loop” is often used in AI governance. It is a reassuring phrase. Machines, we are told, shall not decide alone. But one must ask quietly: whose humanity is present in that loop?

As Shakespeare wrote, “What is the city but the people?” If oversight committees and review boards do not include disability expertise, certain harms will remain invisible. Representation in governance is not ceremonial. It is epistemic.

India stands at a formative moment. Our AI ecosystem is still being shaped. The choices being made now will determine whether exclusion is prevented or automated. If accessibility standards are embedded by default in publicly funded AI systems; if Disability Impact Assessments become routine for high-stakes deployments; if datasets are audited honestly; if disability expertise is included in national AI councils and technical bodies; then India may demonstrate that technological leadership and social Justice are not adversaries.

They may strengthen one another.

If accessibility remains secondary, we shall eventually attempt repair. Repair is always more expensive than foresight.

Hon’ble Prime Minister, artificial intelligence may indeed represent a civilisational opportunity. It is also a moral test. Let Access be built into foundations, not attached later. Let Inclusion be structural, not symbolic. Let Equality be measurable in code, not only declared in speech.

I place these reflections before you with respect and with hope.

Jai Hind. 

Yours sincerely,

Nilesh Singit

TechnoAbleism in India’s AI Moment: Why Accessibility Is Not Enough

When artificial intelligence is built on narrow assumptions of the “normal” user, accessibility features alone cannot prevent exclusion embedded within the algorithm itself.

India’s present moment in artificial intelligence is often described in terms of innovation, opportunity, and national technological leadership. The India AI Impact Summit brings global attention to how artificial intelligence is shaping governance, development, and social transformation. 

Within these discussions, disability is increasingly visible through conversations on accessibility, assistive technologies, and digital inclusion. This attention is important. For many years, disability was largely absent from technology policy debates. Yet, a deeper issue remains insufficiently examined: accessibility alone does not ensure inclusion when artificial intelligence systems themselves are shaped by structural bias.

Accessibility and bias are frequently treated as interchangeable ideas. They are not the same. Accessibility determines whether a person with disability can use a system. Bias determines whether the system was designed with that person in mind at all. When systems are built around assumptions about a so-called normal user, accessible interfaces merely allow disabled persons to enter environments that continue to exclude them through their internal logic. The interface may be open; the opportunity may still be closed.

This structural problem becomes visible in the rapidly expanding practice often called ‘vibe coding’, where developers use generative AI tools to create websites and software through simple prompts. When an AI coding assistant is asked to generate a webpage, the default output usually prioritises visual layouts, mouse-dependent navigation, and animation-heavy design. Accessibility features such as semantic structure, keyboard navigation, or screen-reader compatibility rarely appear unless they are explicitly demanded. The system has learned that the ‘default’ user is non-disabled because that assumption dominates the data from which it learned. As these outputs are reproduced across applications and services, exclusion becomes quietly automated.

Bias also appears in the decision-making systems that increasingly shape employment, education, financial access and public services. Hiring systems that analyse speech, expression, or behavioural patterns may interpret disability-related communication styles as indicators of low confidence or low performance. Speech recognition tools often struggle with atypical speech patterns. Vision systems may fail to recognise assistive devices correctly. These outcomes are not isolated technical errors. They arise because disability is often missing from training datasets, testing environments and design teams. When disability is absent from the design stage, the system internalises non-disabled behaviour as the baseline expectation.

Another less visible dimension of bias emerges from the way artificial intelligence systems classify behaviour. Many systems are trained to recognise patterns associated with what developers consider efficient, confident or normal interaction. When human diversity falls outside those patterns, the system may interpret difference as error. Research in AI ethics repeatedly shows that classification models tend to perform poorly when training datasets do not adequately represent disabled users, leading to systematic misinterpretation of speech, movement or communication styles. 

These classification failures are rarely dramatic; they appear as small inaccuracies that accumulate over time. A speech interface that repeatedly fails to understand a user, an automated assessment tool that consistently undervalues atypical communication, or a recognition system that misidentifies assistive devices can gradually shape unequal access to opportunities. As these outcomes arise from technical assumptions rather than explicit discrimination, they often remain invisible in public debates, even as their effects are widely experienced.

These patterns together reflect what disability scholars describe as techno-ableism - the tendency of technological systems to appear empowering while quietly reinforcing assumptions that favour non-disabled ways of functioning. Technologies may expand participation on the surface, yet the intelligence embedded within them continues to treat disability as deviation rather than diversity. A person with disability may be able to access the interface, log into the system or navigate the platform, yet still face exclusion through hiring algorithms, recognition systems, or automated decision tools that were never designed around diverse bodies and minds. The experience is not exclusion from technology, but exclusion within technology itself.

Public discussions frequently present disability mainly through assistive innovation: tools that help blind users read text, applications that assist persons with mobility impairments or systems designed for specific accessibility functions. These innovations are valuable and necessary. However, when disability appears only in assistive contexts, it is positioned as a specialised technological niche rather than a structural dimension of all artificial intelligence systems. The mainstream design pipeline continues to assume the non-disabled user as the default, while disability inclusion becomes an add-on layer introduced later.

India currently stands at a formative stage in shaping its artificial intelligence ecosystem. As public digital infrastructure, governance platforms and automated service systems expand, the assumptions embedded in present design choices will influence social participation for decades. If accessibility becomes the only measure of inclusion, structural bias risks becoming embedded within the foundations of emerging technological systems. Inclusion then becomes symbolic rather than substantive: systems appear inclusive because they are accessible, yet continue to produce unequal outcomes.

From the standpoint of persons with disabilities, this distinction is deeply personal. Accessibility determines whether we can interact with the system. Bias determines whether the system recognises us as equal participants once we enter. Accessible platforms built upon biased intelligence do not remove barriers; they simply move the barrier from the interface to the algorithm.

As a disability rights practitioner working at the intersection of law, accessibility, and technology, I view the present expansion of AI discussions with cautious attention. Disability is finally visible in national technology conversations, yet the focus remains concentrated on accessibility demonstrations rather than the deeper question of structural bias. Artificial intelligence will increasingly shape employment, governance, education and everyday social participation. Whether these systems expand equality or quietly reproduce exclusion will depend not only on whether they are accessible, but also on whose experiences shape the data, assumptions, and decision rules within them.

Accessibility opens the door; fairness determines what happens after entry. Without confronting bias directly, technological progress risks creating a future that is digitally reachable yet socially unequal for many persons with disabilities. Many of the issues discussed here, including the structural relationship between accessibility and algorithmic bias, are explored in greater detail at The Bias Pipeline (https://thebiaspipeline.nileshsingit.org), where readers may engage with further analysis.

References

• India AI Impact Summit official information portal, Government of India.
• Coverage of summit accessibility and inclusion themes, Business Standard and related reporting.
• United Nations and global policy discussions on AI and disability inclusion.
• Nilesh Singit, The Bias Pipeline https://thebiaspipeline.nileshsingit.org/

(Nilesh Singit is a disability rights practitioner and accessibility strategist working at the intersection of law, governance, and AI inclusion. A Distinguished Research Fellow at the Centre for Disability Studies, NALSAR University of Law, he writes on accessibility, techno-ableism, and algorithmic bias at www.nileshsingit.org)

Moneylife.in
Published 17th Fevruary 202

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