SYLLABUS

GS-2: Government Policies and Interventions for Development in various sectors and Issues arising out of their Design and Implementation.

Context: Recently, the Office of Principal Scientific Adviser (OPSA) released a White Paper titled "Strengthening AI Governance Through Techno-Legal Framework", outlining India’s approach to building a trusted, accountable, and innovation-aligned artificial intelligence (AI) ecosystem.

More on the News

• The publication is the second in the White Paper Series on "Emerging Policy Priorities for India’s AI Ecosystem", an initiative by the OPSA that aims at deepening understanding and fostering informed discussion on critical AI policy issues. 

• The first White Paper in the series, released in December 2025, focused on "Democratising Access to AI Infrastructure", highlighting the need to treat AI infrastructure as a shared national resource and identifying key enablers such as access to high-quality datasets, affordable computing resources, and integration with Digital Public Infrastructure (DPI).

Key Highlights of the White Paper 

• Transition from Traditional “command-and-control” regulation to a "Techno-legal" AI Governance: A practical and ecosystem-wide model that embeds governance directly into the design and operation of AI systems by default and aims to mitigate risks while preserving flexibility and innovation. 

• The framework must also carefully balance privacy safeguards with inclusion, equity, and model utility considerations.

• Key focus areas covered: 

• Understanding the techno-legal approach to AI governance 
• Enabling safe and trusted AI across the full AI lifecycle
• Technological pathways for operationalising techno-legal governance
• Implementation considerations for India’s AI governance framework
• Development of techno-legal tools and compliance mechanisms.

• National database: 

• To record, classify, and analyse safety failures, biased outcomes, security breaches and misuse of AI.
• Enable post-deployment accountability through measures such as India-specific risk taxonomy, detection of systemic trends and emerging threats, data-driven audits and targeted regulatory interventions, and evidence-based refinement of technical and legal controls.

               The national database should have reports on such incidents submitted by public bodies, private entities, researchers, and civil society organisations.

• Measures for AI-Industry: Publish transparency reports, conduct regular fairness and robustness testing, perform security reviews, and have red-teaming exercises.

• The steps allow organisations to build familiarity with compliance processes and documentation before requirements become mandatory.

• Institutional Architecture:

• AI Governance Group (AIGG): chaired by the Principal Scientific Adviser, to harmonise AI governance across ministries, regulators and stakeholders.
• AI Safety Institute (AISI): for evaluating, testing and certifying AI systems to ensure they meet safety and ethical standards.

• Technology and Policy Expert Committee (TPEC): To support the functioning of AIGG, the Ministry of Electronics and Information Technology should set up a dedicated Technology and Policy Expert Committee (TPEC), which could pool multidisciplinary experts from law, public policy, machine learning, AI safety, cybersecurity, and public administration, among other things. 

• Impact-aware data withdrawal mechanisms: To mitigate the risks of privacy, exclusion and inequity in a country that is linguistically and demographically diverse, impact-aware data withdrawal mechanisms are needed rather than unconditional erasure. 

• Large-scale unlearning requests should be subject to fairness and representativeness impact assessments, with safeguards triggered where data removal risks degrade performance for underrepresented groups.

Significance of the Report

• Nuanced AI governance framework: It highlights India’s pro-innovation approach to AI governance, which integrates baseline legal safeguards, sector-specific regulations, technical controls, and institutional mechanisms.
• Regulatory and Technological Necessity: Developing a robust and responsive governance framework is not just a regulatory necessity but a prerequisite for sustaining the momentum of technological progress. The techno-legal approach offers a viable pathway by embedding legal, technical, and institutional safeguards into AI systems by design.

• Attempts to solve “Pacing Problem”: By embedding governance into system design rather than relying purely on post-hoc enforcement.
• Shaping Global AI governance: These White Papers, framed as explanatory knowledge documents, are intended to support informed deliberations on various policy priorities to shape the evolving AI ecosystem and reinforce India’s catalytic role in shaping the global AI governance discourse.
• Integration with India’s Digital Ecosystem: The white paper discusses leveraging Digital Public Infrastructure (DPI) and the Data Empowerment and Protection Architecture (DEPA) for trustworthy, consent-based data access and governance.