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All glossary terms

AI governance

Bias Detection

Bias detection is the process of looking for systematic differences or unwanted patterns in a dataset, model, or automated decision process. It uses methods chosen for the task and available evidence, and findings require interpretation before anyone concludes that a system is fair or unfair.

Also known as bias testing.

Why it matters in practice

Useful detection begins with a clear outcome, population, and comparison, then examines the data and model at appropriate stages. Reviewers should note sample limitations and distinguish a signal from a causal explanation. Detection can inform remediation, but it does not independently select the right fairness standard or control.

Example

An analyst compares false-positive rates across relevant groups, checks sample sizes, and traces a difference back through labels and decision thresholds.

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.