In this episode of Interpreting India, Nidhi Singh, speaks with Professor Balaraman Ravindran, Head of the Department of Data Science and AI at IIT Madras, and Co-Chair of the Safe and Trusted AI Working Group at the India AI Impact Summit. Since the summit, Professor Ravindran has also been appointed to the UN's Independent International Scientific Panel on AI. There is a narrative that has taken hold since the summit, that India moved away from safety and left frontier risks behind. This conversation sets the record straight.
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This episode is part of our special series on the India AI Impact Summit, examining the conversations, decisions, and debates that are shaping global AI governance.
In this episode of Interpreting India, Nidhi Singh, speaks with Professor Balaraman Ravindran, Head of the Department of Data Science and AI at IIT Madras, and Co-Chair of the Safe and Trusted AI Working Group at the India AI Impact Summit. Since the summit, Professor Ravindran has also been appointed to the UN's Independent International Scientific Panel on AI. There is a narrative that has taken hold since the summit, that India moved away from safety and left frontier risks behind. This conversation sets the record straight.
What did the Safe and Trusted AI Working Group actually deliver, and what are the Trusted AI Commons? And the AI governance guidance note designed to do? Was the India AI Impact Summit really less focused on safety, or did the conversation simply evolve when it moved to the Global South? How quickly is the frontier risk landscape changing, and are the frameworks we are building keeping pace? What does the growing concentration of the most capable AI models in the hands of two countries mean for a country like India?
Professor Ravindran addresses early on the perception that the India summit sidelined safety. More than 60% of the summit's events and discussions were focused on safety, trust, and cross-border collaboration. The framing shifted, and deliberately so. When the summit came to the Global South, leading with existential risk, rather than the very real opportunity AI presents to improve healthcare, education, and public services for hundreds of millions of people, would have been the wrong entry point. The two key deliverables from his working group reflect that balance: the Trusted AI Commons, a repository of benchmarks, testing protocols, and best practices designed for AI deployment in resource-constrained settings, and a high-level governance guidance note endorsed by 22 countries, that calls out the issues every national AI policy should address without being prescriptive enough to limit how different countries approach it.
On frontier risks, Professor Ravindran notes that the landscape has shifted in ways that would have seemed speculative even a year ago, and that the frameworks being built to manage these risks will need to keep pace with that change. He also reflects on what the growing concentration of the most capable AI models means for countries like India, and why that conversation may need to move from being a company-to-country dialogue to a country-to-country one. His overall view is one of cautious optimism: there will be disruption in the short term, but there will also be a new equilibrium, and the work is to make sure the transition is managed well.
Carnegie India does not take institutional positions on public policy issues; the views represented herein are those of the author(s) and do not necessarily reflect the views of Carnegie, its staff, or its trustees.
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