The gap between the number of experimental AI initiatives and the number deployed at scale across industries and governments is growing exponentially. As this gap grows, so does the risk of biased algorithms, opaque decision-making processes, violations of consumers' right to privacy, misuse of AI technology, and the continued erosion of public trust in AI technologies' ability to function safely, fairly, and reliably.

This session views Responsible AI as an engineering profession with specific practices and operational rigor versus as aspirational policy or a checklist of regulatory compliance. The discussion will focus on how safety, trust, inclusiveness and accountability must be incorporated throughout each stage of the AI life cycle (data, training models, deploying production, operational management) to build scalable responsible AI technologies that maintain public trust and deliver sustained societal benefit.

INDIAai Pre-Summit 2026