AI on the Ground: A Snapshot of AI Use in India

This report presents an exhaustive study on the current uses of AI in India, reviewing 70+ use cases across 9 key sectors, including policing, agriculture, health, and education.

Emerging discourses around the use of AI in India are polarised. On the one hand, a narrative of AI solutionism has taken hold, where AI is seen as a silver bullet to address complex and persistent problems of development, like health and education. At the other extreme, AI is seen as a grave threat to civil liberties and job futures. Discussions on AI governance also refer to broad principles, such as fairness and equity, which can have differing implications across diverse social contexts.

With this study, we take a snapshot view of the various ways in which AI is in use across key sectors in India, in order to determine which areas of application require public scrutiny and debate. Innovations that deliver value to society require the social shaping of technology to ensure that the technological trajectories are equitable and add value at all levels of society. Public scrutiny and policy steering of AI trajectories will therefore be critical.

What is AI used for in India, in different sectors? What are the benefits and challenges? Which areas of AI use requires public scrutiny and policy steering?

  • Small but incremental benefits are accruing from the deployment of AI and ML-enabled systems.
  • Narratives of the transformative impacts of AI are yet to be matched by current use cases.
  • It is not simply the technology, but its use that also requires closer public scrutiny.
  • Many development and deployment challenges are similar across sectors.
  • Greater oversight is needed when Machine Learning (ML) applications are central
    to decision- making about people, their rights, livelihoods, and relationships.
  • ML systems that enable the profiling of individuals and groups require adequate checks and balances.
  • The use of AI in public service systems and safety-critical sectors should be held to higher standards of transparency and accountability.
  • Monopolisation of data and differential access to resources to build ML systems increases market inequities.
  • Uneven distribution of technology gains can entrench existing societal inequities and create new ones.
  • Policy interventions should be based on an evaluation of the social impact of ML applications.
  • Investments in state and regulatory capacity, along with analog components of digital society are needed.
  • Red lines should be drawn around certain types of use.
  • Data protection frameworks need to be accompanied by community rights and accountability frameworks.
  • Risk management approaches must be accompanied by upstream management of technological innovation processes.


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