Definition
AI bias is a systematic tendency in data, modeling, or decision processes that skews estimates, errors, treatment, or impacts. Bias can enter through who or what is measured, how labels are assigned, which objective is optimized, what proxy is chosen, how a threshold is set, where the system is deployed, and which feedback becomes new data.
Analyze mechanism and consequence
Different group outcomes are a signal to investigate, not a complete diagnosis. Population base rates, label quality, measurement error, access, threshold choice, and prior inequity may all contribute. Report performance by relevant subgroup and context, examine who bears false positives and false negatives, and involve people with domain and affected-community knowledge in deciding what fairness requires.
Why it is contested
The word names different concepts. In statistics, an estimator is biased when its expected value systematically differs from the quantity being estimated. In machine learning, inductive bias is the set of assumptions that lets a learner generalize. In governance, bias often means systematic disadvantage or distortion affecting people or groups. Those meanings can interact, but they use different baselines and do not make the same claim. A useful diagnosis must state which meaning applies and what evidence establishes it.
Distinguish it from nearby terms
Fairness is a normative judgment about acceptable treatment and outcomes. Discrimination is unequal treatment or impact with legal and social meanings. Bias is one possible mechanism. A model can satisfy one fairness metric while violating another because the metrics encode incompatible choices.
Check your understanding
A hiring model has equal overall accuracy across groups but rejects qualified applicants in one group more often. Is "accuracy is equal" enough? No. Inspect false-negative rates, label and selection history, decision thresholds, job relevance, affected people, and the cost of each error.