As India rejects IMF's AI Preparedness Index, NIPFP economists plan AI Composite Index
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Researchers associated with New Delhi-based National Institute of Public Finance and Policy (NIPFP) are working on an Artificial Intelligence (AI) Composite Index that incorporates the best of existing measurements and datasets, including International Monetary Fund’s AI Preparedness Index (AIPI) and Stanford University’s Human-Centered Artificial Intelligence (HAI) AI Index and more, to provide a ‘clearer picture’ of how countries are AI equipped in a ‘policy-relevant’ manner.
The attempt comes as India disagrees with International Monetary Fund’s AI Preparedness Index (AIPI) that puts the country in the 72nd position among 174 economies AIPI tracks and agrees with the Stanford University rankings, which place India third globally in AI penetration and preparedness, and second in AI talent.
Union Minister for Railways, Information & Broadcasting, Electronics & Information Technology Ashwini Vaishnaw was the latest person to find fault with AIPI and appreciate HAI at the World Economic Forum meeting in Davos on Wednesday.
AI composite Index
Meanwhile, NIPFP professor Lekha Chakraborty and intern Rohan Dubey state their forthcoming study on the AI composite Index will build on the existing foundations and integrate three core pillars to capture both supply-side leadership and enabling conditions for broad adoption. The core pillars are AI patent registrations per million population from the World Intellectual Property Organization (WIPO), venture capital investment in AI as a share of GDP, drawing on OECD and complementary sources and IMF’s AI Preparedness Index.
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In a blog published by NIPFP, Dubey and Chakraborty said to construct the index, skewed distributions in patents and investment are addressed through logarithmic transformation for better balance. “Each component is then standardised to have comparable spread, ensuring no single pillar dominates due to measurement differences. The index takes a simple average of the three, with equal weights for transparency and to avoid strong prior judgments on relative importance. Finally, scores are rescaled from zero to one for easy interpretation, without changing country rankings. Robustness checks confirm stability”, the authors point out.
Actionable insights
For policymakers, the index offers actionable insights, they say. “Tracking these dimensions helps identify bottlenecks—whether low investment signaling weak entrepreneurial confidence or preparedness gaps hindering broad uptake. Future enhancements could incorporate user adoption metrics or gender-disaggregated data to better capture inclusive diffusion”, the authors say.
According to them, in a fast-evolving field, systematic measurement is vital. “By blending established indicators into a transparent composite, the AI Composite Index provides a practical tool for monitoring progress and designing policies that make AI benefits widely shared”, they argue.