Fairness

A Four-Phase Framework for Finding and Fixing Bias in AI Systems

This article presents a practical, four-phase framework for systematically identifying and mitigating bias in AI systems across their entire development lifecycle. It details strategies for ethical foundations, data preparation, model validation, and continuous post-deployment monitoring to ensure fairer AI outcomes.