Part 12/13:

• Adaptive Governance: System architecture must adapt to evolving regulations, policies, and societal expectations.

• Bias Prevention & Fairness: Continuous checks are necessary at every stage—data input, model training, inference, and post-deployment—to prevent bias.

He emphasizes multi-layered checks—from data collection and enrichment to inference and feedback—ensuring AI systems act ethically, fairly, and transparently.

Final Thoughts

Harish's session offers a thorough blueprint for developing AI systems responsibly. The takeaways focus on what is often overlooked: ethical data collection, contextual understanding, bias mitigation, transparency, and ongoing accountability.