Bias can come from unrepresentative data, labels that reflect past discrimination, proxies for protected characteristics or evaluation that ignores some groups. It shows up in facial recognition error rates, hiring tools and stereotyped images from generators.
Mitigation starts with measuring performance across groups and continues with better data, fairness constraints and human oversight. Read algorithmic bias explained.