Part 11/13:

A core responsibility of responsible AI is traceability. Harish advocates for systems that log and audit all AI decision processes, including the reasoning behind responses. This creates a transparent environment where biases or errors can be identified and addressed.

He stresses that AI models, especially Large Language Models (LLMs), are inherently nondeterministic; thus, maintaining logs ensures that deviations can be analyzed and rectified. Proper documentation supports human oversight and regulatory compliance.

Closing Principles: Proactive Governance and Continuous Improvement

To conclude, Harish distills responsible AI into three pillars:

• Explainability & Traceability: Every decision should be auditable and human-understandable.