Bias in AI is a systematic skew in data, model behavior, evaluation, design, or deployment that can produce unfair, inaccurate, or harmful outcomes.
Bias can arise from training data, labels, system design, metrics, deployment context, and human decisions; it is not limited to intentional discrimination.