What AI Accountability Should Mean
AI accountability is often misunderstood as a regulatory barrier to innovation. In reality, it is the opposite — it is the trust infrastructure that allows innovation to scale sustainably. For a country like India , where technology rapidly impacts hundreds of millions of citizens, accountability cannot be optional. It must be designed into the system from the beginning. A practical national framework can be structured around five core pillars: 1. Explainability Rights Every individual affected by an AI-driven decision should have the ability to understand the basis of that decision. At a minimum, citizens must be able to know: Why the decision was made What categories of data were considered Whether human supervision or review was involved Transparency converts automated decisions from opaque outcomes into accountable processes. 2. Algorithm Audits High-impact AI systems — particularly those used in finance, recruitment, healthcare, insurance, and governance — sh...