Whalesbook
India's Push for a Tailored AI Preparedness Measure
Researchers from the National Institute of Public Finance and Policy (NIPFP) are actively constructing a novel Artificial Intelligence (AI) Composite Index. This initiative aims to provide a 'clearer picture' of how countries are equipped for AI in a manner that is directly applicable to policy-making. The development comes as India expresses a divergence of opinion with the International Monetary Fund’s (IMF) AI Preparedness Index (AIPI). The AIPI's assessment, which ranked India 72nd out of 174 economies, has been met with disagreement. Conversely, India finds greater alignment with the rankings provided by Stanford University's Human-Centered Artificial Intelligence (HAI) AI Index, which places India third globally in AI penetration and preparedness, and second in AI talent. Union Minister for Railways, Information & Broadcasting, Electronics & Information Technology, Ashwini Vaishnaw, has publicly voiced his critique of the IMF's AIPI while endorsing the Stanford rankings, notably at the World Economic Forum meeting in Davos.
Methodology and Pillars of the NIPFP Index
NIPFP professor Lekha Chakraborty and intern Rohan Dubey are spearheading the creation of this forthcoming study. Their AI Composite Index will build upon existing frameworks by integrating three core pillars designed to capture both leadership in AI supply and the conditions conducive to widespread adoption. These pillars include:
- AI Patent Registrations: Data sourced from the World Intellectual Property Organization (WIPO) on AI patent registrations per million population.
- Venture Capital Investment: Information on venture capital investment in AI as a share of GDP, drawing on data from the OECD and other sources.
- IMF AI Preparedness Index: The IMF's own AIPI will also be incorporated as a component.
To ensure robustness and comparability, the researchers employ specific data processing techniques. Skewed distributions in patent and investment data are addressed through logarithmic transformation. Each component is then standardized to achieve a comparable spread, preventing any single pillar from dominating due to measurement differences. The index is constructed as a simple average of these three pillars, assigning equal weights for transparency and to avoid pre-determined biases on relative importance. Finally, scores are rescaled from zero to one for ease of interpretation, without altering country rankings.
Actionable Insights for Policy Formulation
The index is being developed to offer actionable insights for policymakers. Tracking these dimensions is intended to help identify potential bottlenecks, whether they relate to low investment signaling weak entrepreneurial confidence or to preparedness gaps hindering broad AI uptake. The authors suggest future enhancements could include user adoption metrics or gender-disaggregated data to better represent inclusive diffusion. In a rapidly evolving field like artificial intelligence, systematic measurement is deemed vital. By blending established indicators into a transparent composite, the NIPFP's AI Composite Index aims to serve as a practical tool for monitoring progress and guiding the design of policies that promote the equitable sharing of AI benefits.
Minister Vaishnaw's stance at Davos reinforces India's position, asserting that the nation belongs to the 'first group' of AI powers, citing Stanford's rankings which place India third in AI preparedness and second in AI talent, contrary to the IMF's assessment. He emphasized India's focus on development across all five layers of AI architecture—application, model, chip, infrastructure, and energy—and stressed a strategy centered on widespread deployment and return on investment over solely creating large models.