AADHAAR-UPI MODEL: INDIA’S BLUEPRINT FOR SOVEREIGN AI FUTURE

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Context

India's successful large-scale technology implementation, like Aadhaar and UPI, using Digital Public Infrastructure (DPI) suggests it can adapt these principles to create an inclusive, sovereign, and innovative AI ecosystem

 India's DPI Model: A Blueprint for Inclusive AI

India's success in building Digital Public Infrastructure (DPI) like Aadhaar and the Unified Payments Interface (UPI) offers a unique and powerful blueprint for developing trustworthy, inclusive, and population-scale Artificial Intelligence (AI). 

This model, focused on creating open, interoperable, and low-cost digital "rails," provides a strategic alternative to the current global AI landscape dominated by a few private tech giants.

The Aadhaar-UPI Model: Lessons for AI Design

India's layered DPI, the India Stack, enables system connection and innovation—a key ecosystem approach for public-benefit AI.

Aadhaar: Solving the Identity Barrier

Population-Scale Identity: Aadhaar provided a unique digital identity to over 1.4 billion residents, creating a foundational layer for digital services.

Plugging Welfare Leakages: Aadhaar-enabled Direct Benefit Transfer (DBT) has saved the government over ₹3.48 lakh crore by March 2025 by eliminating fake and duplicate beneficiaries. (Source: PIB).

Authentication Layer: It provides a simple, low-cost mechanism for online authentication (e-KYC), which has been crucial for financial inclusion and telecom penetration.

UPI: Revolutionizing Financial Transactions

Open and Interoperable: UPI allows any certified entity (banks, fintech apps) to build payment services on top of it. This prevents a monopoly and promotes competition.

Large Scale: UPI constitutes 85% of India's digital transactions and drives almost 50% of global real-time digital payments. (Source: PIB).

Private Innovation on Public Rails: This model shows how a public platform promotes massive private innovation, a key to the AI ecosystem.

Core Principles for India's AI Path

Openness and Interoperability: Using open standards and protocols to ensure universal access to AI tools for startups and researchers.

Collaborative Public-Private Partnership (PPP): Government to create foundational infrastructure, datasets, and platforms, while private companies build customer-facing applications and specialized solutions.

Inclusion and Affordability: Systems must be low-cost, accessible, and designed to serve the entire population, including multilingual users and marginalized communities.

Global AI Landscape 

The global AI ecosystem offers India challenges and a unique opportunity.

• Market Concentration: A few large US and Chinese corporations dominate the development of powerful foundational AI models. USA company NVIDIA holds a near-monopoly (92% market share) in data center GPUs, the critical AI infrastructure.
• High Compute Costs: Training these models demands vast computational power, creating high entry barriers for developing countries and smaller players.
• Ethical Concerns: Algorithmic bias, lack of transparency, data privacy, and potential monopolies are major global concerns.

India can use its DPI success to create a unique, open, democratic, and public-benefit AI model, distinct from the market-driven US and state-controlled Chinese approaches.

Way Forward for India 

India's IndiaAI Mission, with an approved budget of ₹10,372 crore, prioritizes developing "AI rails" or public digital platforms over massive, compute-intensive foundational models.

Key Components of the "AI Rails"

• Standardized & Open Datasets: Creating extensive, anonymized datasets for critical sectors (healthcare, agriculture, languages) for startups and researchers, accessible under strict privacy.
• Shared Compute Infrastructure: Democratizing AI development by building public facilities for affordable access to essential computing resources like GPUs.
• Open-Source AI Models: Encouraging smaller, domain-specific, and multilingual AI models for India's diverse needs, exemplified by the Bhashini mission for Indian language technologies.
• Safety and Alignment Frameworks: Set clear standards and sandboxes to test AI applications for safety, fairness, and transparency before public service deployment.

Establishing Trust, Safety, and Accountability in AI

Drawing from the governance challenges of the Aadhaar rollout—where initial privacy concerns necessitated corrective action via Supreme Court rulings—India must proactively develop a robust regulatory framework for AI.

Guiding Principles for AI Regulation

AI Applications for Public Welfare and Governance

Building on DPI foundation, India is positioned to deploy AI to transform public service delivery and administration:

• Healthcare: Utilizing AI for diagnostics in rural settings, developing early warning systems for disease outbreaks, and enabling personalized medicine.
• Agriculture: Delivering precision farming advice based on satellite and weather data, and improving crop insurance assessments.
• Governance: Facilitating real-time monitoring of key schemes (e.g., MGNREGA), optimizing urban planning through traffic and resource management, and offering multilingual governance access via AI translation.
• Education: Creating personalized learning tools for students and AI assistants to reduce administrative tasks for teachers.

However, the risks associated with over-automation, exclusion of the digitally illiterate, and algorithmic bias must be actively managed through strong oversight and citizen-centric design.

Source: INDIAN EXPRESS