Strengthening AI Governance Through Techno-Legal Framework

Syllabus: GS2/ Governance, GS3/ Science and Technology

Context

• The Office of the Principal Scientific Adviser (OPSA) to the GOI has released a White Paper titled “Strengthening AI Governance Through Techno-Legal Framework”, outlining India’s approach to building an accountable, and innovation-aligned artificial intelligence (AI) ecosystem.

Techno-Legal AI Governance

• The techno-legal approach integrates legal instruments, regulatory oversight, and technical enforcement mechanisms directly into the design and operation of AI systems.
  • Governance is treated as an intrinsic feature of AI systems, rather than an external compliance obligation.
• This approach ensures that AI systems, whether developed domestically or sourced globally, remain aligned with India’s constitutional values, legal norms, and developmental priorities.

Rationale for a New AI Governance Framework

• Artificial Intelligence is adaptive, opaque, rapidly evolving, and borderless, making traditional command-and-control regulation inadequate.
• Existing Indian regulations such as the Information Technology (IT) Act, 2000, DPDP Act, 2023, Bhartiya Nyaya Sanhita (BNS), 2023, sectoral guidelines, and voluntary standards provide baseline safeguards but are not designed to address AI-specific lifecycle risks.
• There is a need for a governance model that prevents harm proactively, rather than relying on post-facto legal enforcement.

Objectives of the Techno-Legal Framework

• The framework seeks to uphold fundamental rights such as privacy, security, safety, access to fair information, and livelihood protection in the AI era.
• It aims to ensure that AI systems are trained, deployed, and used in a manner that guarantees fair treatment and non-discrimination.
• The framework balances innovation and safety, rejecting the false binary of “innovation versus regulation.”

Technological pathways to Techno-Legal AI Governance

• The IndiaAI Mission, under its “Safe and Trusted AI” pillar, reflects India’s shift towards embedding legal, ethical, and safety safeguards directly into AI systems.
• In 2024, MeitY launched a national “Responsible AI” call, selecting indigenous solutions for operationalising AI governance across government and industry.
• AI Auditing Tools:
  • Nishpaksh (fairness audits) and ParakhAI (participatory algorithm audits).
  • Track-LLM for governance testing of large language models.
• Integration with Digital Public Infrastructure (DPI): Integration of techno-legal AI tools with India’s Digital Public Infrastructure (DPI) enhances scalability and enforceability.
  • Platforms such as Aadhaar, DigiLocker, and UPI provide secure, interoperable foundations for embedding governance mechanisms.

Challenges in Operationalising Techno-Legal AI Governance

• AI-Subject vs AI-User Asymmetry: In welfare domains such as healthcare, education, and public safety, affected individuals are often AI subjects, not users.
  • AI subjects usually lack awareness, consent, or effective means to contest algorithmic decisions, increasing risks of exclusion and injustice.
• Deepfake Governance Limitations: Content-level takedowns are insufficient, as deepfakes operate through distributed pipelines involving generation tools, platforms, bots, and infrastructure providers.
  • Rapid re-upload, domain migration, and cross-platform amplification weaken conventional enforcement.
• Cost Constraints: Techno-legal compliance imposes high costs on firms due to audits, security upgrades, skilled personnel, and data infrastructure.
• Legal and Operational Misalignment: Rapidly evolving laws on data protection, IP, and AI governance create uncertainty in implementation.

Way Ahead

• AI-Subject-Centric Governance: Mandate algorithmic impact assessments, proactive disclosure of AI use, and human-in-the-loop mechanisms at critical decision points.
  • Establish grievance redressal systems and regular demographic audits for subject-facing AI applications.
• Deepfake Regulation: Adopt content provenance mechanisms such as mandatory labeling, persistent identifiers, and cryptographic metadata.
  • Impose infrastructure-level obligations like usage logging, repeat-offender detection, and coordinated incident reporting.
• Capacity Building: Invest in interdisciplinary training, shared testing environments, and open-source risk assessment tools.

Source: PIB