PSA white paper calls for a national AI incident database
India's Office of the Principal Scientific Adviser (PSA) has proposed a national database to record, classify, and analyse AI incidents. The scope covers safety failures, biased outcomes, security breaches, and misuse-reported by public bodies, private firms, researchers, and civil society.
The goal is clear: enable post-deployment accountability. A single, India-specific system would surface systemic trends, support data-driven audits, inform targeted regulatory action, and refine both technical and legal controls over time.
What the database should capture
- Incident types: safety failures, bias, security breaches, misuse.
- Contributors: government departments and agencies, regulated private entities, researchers, and civil society organisations.
- Classification and analysis: India-specific risk taxonomy, sector and use-case tagging, severity, affected populations, and remediation status.
- Outcomes: lessons learned, follow-up actions, and signals for policy updates.
Voluntary measures industry should start now
The white paper recommends steps that build compliance muscle before mandates arrive. These practices also surface risks earlier and raise sector-wide capacity.
- Publish regular transparency reports.
- Run fairness and resilience testing on deployed models.
- Conduct structured security reviews.
- Perform red-teaming exercises and document fixes.
Governance setup to make this work
To support the national AI governance group chaired by the PSA, the paper proposes a Technology and Policy Expert Committee (TPEC) under the Ministry of Electronics and Information Technology (MeitY). TPEC would pool experts in law, public policy, machine learning, AI safety, cybersecurity, and public administration to guide implementation and oversight.
Learn more about MeitY's role in digital governance on the MeitY website, and the PSA's mandate on the Office of the PSA.
Privacy, fairness, and model utility: choosing trade-offs with intent
The paper stresses a practical reality: privacy safeguards, fairness, and model performance often pull in different directions. In a linguistically and demographically diverse country, those trade-offs cannot be left to default engineering choices.
Recommended approach: use impact-aware data withdrawal instead of blanket erasure. Large-scale unlearning requests should go through fairness and representativeness assessments, with safeguards if removal harms performance for underrepresented groups.
What government teams can do next
- Nominate nodal officers for AI incident reporting and response within each ministry/department.
- Pilot a lightweight incident submission workflow (internal first, then with select external partners).
- Adopt a shared risk taxonomy and severity scale across agencies to enable consistent reporting.
- Set up red-team drills for high-impact AI systems in public services and critical infrastructure.
- Define guardrails for data withdrawal and unlearning that protect inclusion while respecting privacy.
- Establish KPIs: time to detect, time to contain, recurrence rate, and audit completion rate.
- Partner with academia and civil society for independent testing and incident validation.
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