India’s DPI Gambit: Can It Redefine Global AI Governance?

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Much like the nuclear age or the space race, the AI era is defined by a scramble for strategic control—this time over critical hardware, data, and computational power—creating divisions in governing this transformative technology. 

Amid this chaos, New Delhi is steadily pushing for the “DPI-sation” of international AI governance architecture through open and diverse datasets and common design principles. India’s historical position as a bridge power (e.g., atomic energy and environment) and its successful stint with adopting digital public infrastructure (DPI) solutions across various sectors make it a natural leader not only in promoting such integration but also in bridging the divergences in approaches of the global North and global South. This is further strengthened amid the rising internationalisation of DPI and the interest of the Global South and European countries in promoting an inclusive digital push in the public and private sectors. Therefore, this piece analyses how India is promoting the DPI-sation of AI governance at the international level and what challenges lie ahead in achieving its goals. 

Integration of AI with DPI

DPI-sation in the context of this article is identified as the process of integrating foundational principles of DPI, such as open, interoperable, and scalable design, into international negotiations on AI governance. 

DPI-sation could flow into two strands: technical approaches to implement AI into the existing DPI ecosystem, and shaping political discussions around DPI as the sine qua non feature of the global AI governance architecture. 

The former focuses on the ways AI, as a general-purpose technology, could be implemented within DPI by utilising its rich data infrastructure and ability for scalable deployment to serve public needs across sectors. A good example could be India’s BHASHINI AI, which detects and translates multiple languages, allowing the DPI ecosystem to reach the far corners of the country. Similarly, Singapore and Denmark are implementing AI within their DPI ecosystems to detect fraud and improve public services, respectively. 

The latter aspect argues for integrating DPI as a core principle into the skeleton of any AI governance architecture, which democratises AI as a public-use technology in international negotiations and gives equitable access to diverse datasets and hardware to developing countries. 

Both aspects broaden the range of countries interested in governance negotiations and give them the flexibility to converge their national interests to solve the dilemma of common interests (proposed by Stein)—“to deal with the collective suboptimality that can emerge from individual behaviour” of the state when it comes to AI governance. 

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