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All glossary terms

AI governance

AI Fairness

AI fairness is the evaluation of whether an artificial intelligence system treats people or groups acceptably under a chosen fairness criterion. The relevant criterion depends on the decision, data, and affected population; different metrics can conflict, so the term does not imply one universal test or outcome.

Also known as algorithmic fairness.

Why it matters in practice

Fairness work begins by understanding the decision and who may be affected, then selecting defensible measures and examining data and system behavior. Results need interpretation alongside domain knowledge and qualitative review. A fairness label or a single benchmark score cannot prove that a model is unbiased across all uses or groups.

Example

A hiring team compares error patterns across relevant groups, reviews the data and task design, and records why the selected measure fits the decision.

Educational note

This definition is general information, not legal advice. It may omit facts that matter to a specific situation and does not establish or guarantee compliance.