When a Face Scan Meets Food Security: AI, Welfare, and the Future of Digital Governance in India

A Test Case for AI in Governance

India’s ambition to lead global conversations on the responsible use of artificial intelligence will ultimately be judged not in policy forums, but in everyday governance systems. Few are as consequential as the Public Distribution System (PDS), which serves over 800 million people under the National Food Security Act. It is both a logistical achievement and a moral commitment, quietly sustaining livelihoods across the country.

Recent policy thinking has positioned AI as a way to strengthen this system. The idea is to move from digitised delivery to more responsive, intelligent delivery, where technology supports decision-making across the value chain, from procurement and storage to logistics and last-mile distribution (Patra & Sadasivan, 2026). In principle, this approach offers significant promise. Better demand forecasting, improved coordination and more effective grievance redressal could enhance both efficiency and service quality. Importantly, it also recognises that AI should assist governance, not replace it.

The promise is clear. The question is how it unfolds in practice.

Learning from Early Implementation

Early experiences with AI-enabled tools in welfare systems offer valuable lessons. The use of facial recognition for authentication in programmes such as the Integrated Child Development Services (ICDS) provides insight into both the potential and the challenges of such technologies. Field reporting highlights instances where authentication has not always worked as intended, leading to delays or difficulties in accessing services (Rizwan, 2026).

These challenges often stem from practical factors. Photographs used for verification may be outdated, while real-world conditions such as lighting, device quality and connectivity can affect accuracy. Such issues are not unique to India; they are common in the deployment of emerging technologies across diverse and resource-constrained settings.

Rather than viewing these as failures, they can be understood as part of an iterative process of policy learning. They point to areas where systems need to be refined, infrastructure strengthened and safeguards reinforced.

Balancing Efficiency with Inclusion

At the heart of the discussion on AI in welfare is the need to balance efficiency with inclusion. Reducing leakages and improving targeting are important objectives. At the same time, welfare systems are designed to ensure access, particularly for those who depend on them most.

Policy perspectives have rightly emphasised that AI should not become the final arbiter of entitlement, and that human oversight and offline alternatives must remain integral to the system (Patra & Sadasivan, 2026). This principle is essential for maintaining the rights-based character of welfare programmes.

The task, therefore, is not to choose between efficiency and inclusion, but to design systems that advance both. With careful calibration, technology can support more accurate delivery while preserving flexibility at the last mile.

Supporting Frontline Governance

An important consideration in this transition is the experience of frontline workers. Welfare delivery depends heavily on individuals such as Anganwadi workers and fair price shop dealers, who serve as the interface between the state and citizens.

The introduction of new technologies has added responsibilities for these actors, including managing authentication processes and addressing system-related issues. While this can initially increase workload, it also highlights the need for stronger institutional support. Training, reliable infrastructure and user-friendly system design can ensure that technology becomes a tool that assists rather than burdens frontline workers (Rizwan, 2026).

Investing in frontline capacity is therefore central to the success of digital governance reforms. When workers are equipped and supported, they can play a key role in making systems more responsive and inclusive.

Building Transparent and Accountable Systems

As AI becomes more integrated into welfare delivery, questions of transparency and accountability gain importance. Clear information about how systems function, how decisions are made and how errors are addressed can strengthen public trust.

Policy discussions have already highlighted the importance of explainable models, audit mechanisms and responsible data practices (Patra & Sadasivan, 2026). Embedding these principles into system design can ensure that technology remains aligned with democratic values.

Equally important is the development of accessible grievance redressal mechanisms. When beneficiaries can easily report issues and receive timely responses, systems become more adaptive and resilient. In this sense, feedback is not merely corrective but also a source of continuous improvement.

Addressing Uneven Realities

India’s diversity means that technological interventions operate across a wide range of social and infrastructural contexts. Connectivity, digital literacy and levels of dependency on welfare systems vary significantly across regions and populations.

Evidence suggests that these differences shape how people experience digital systems. For some, minor inconveniences may be manageable. For others, particularly those in more vulnerable situations, even small disruptions can have significant consequences (Rizwan, 2026; Patra & Sadasivan, 2026).

Recognising this diversity is essential for policy design. Systems that incorporate flexibility, multiple modes of access and context-sensitive implementation are better suited to serve a wide range of users. Inclusion, in this sense, becomes a design principle rather than an outcome.

Towards Responsible and Responsive AI

The PDS offers a valuable opportunity to demonstrate how AI can be integrated into governance in a way that enhances both efficiency and equity. Its scale, complexity and centrality to everyday life make it an ideal test case.

A constructive way forward lies in focusing on three priorities. First, ensuring that inclusion remains central, with safeguards such as offline options and human oversight firmly in place. Second, strengthening transparency and accountability through open systems and independent oversight. Third, grounding policy in ongoing learning, using field-based evidence to refine and improve implementation.

These steps do not require abandoning technological ambition. Rather, they call for aligning innovation with the realities of governance and the needs of citizens.

Conclusion: Strengthening the Social Contract

India’s journey towards AI-enabled governance reflects both aspiration and opportunity. The potential to make welfare systems more responsive, efficient and resilient is significant. At the same time, early experiences highlight the importance of careful design, thoughtful implementation and continuous evaluation.

Welfare systems are, at their core, expressions of the social contract. They represent a commitment to ensuring that essential services reach those who need them. Technology can play a powerful role in strengthening this commitment, provided it is deployed with care and responsibility.

If India succeeds in integrating AI into systems such as the PDS in a way that enhances reliability while upholding dignity and rights, it will not only improve domestic governance but also offer a meaningful example for the world. The true promise of digital governance lies not just in innovation, but in its ability to serve people effectively and equitably.

References

Patra, S., & Sadasivan, S. (2026, March 28). AI in public distribution system India | A perfect test case. The Telegraph Online. https://www.telegraphindia.com/opinion/a-perfect-test-case-ai-set-to-transform-indias-public-distribution-system-prnt/cid/2153509#goog_rewarded

Rizwan, H. (2026). How I investigated the use of facial recognition in India’s flagship welfare programme. Pulitzer Center. https://pulitzercenter.org/resource/how-i-investigated-use-facial-recognition-indias-flagship-welfare-program