Fair Ai

Diagnosing AI Bias and Choosing the Right Fix: A Lifecycle-Based Framework

This article presents a lifecycle-based framework for diagnosing AI bias, emphasizing that fixes must match the stage of bias origination: pre-processing data, in-processing model training, or post-processing outputs. It guides teams through identifying, measuring, and mitigating bias for sustained fair AI.