Part 10/13:
Harish walks through the architecture of a typical AI inference pipeline, emphasizing proactive bias detection and fairness checks. Data is enriched and processed in multiple stages, with an eye towards demographic balance and error mitigation throughout the system's lifecycle.
The importance of feedback loops is highlighted—where the system learns from past errors or biases—for continuous improvement. His mention of prompt refinement and system explainability points toward fostering systems that can justify their decisions, which is critical for compliance and user trust.