Skip to main content
All glossary terms

AI governance

Algorithmic Bias

Algorithmic bias is a systematic pattern in an automated system that can produce uneven or unjust outcomes. It may stem from data collection, labeling, model design, thresholds, or deployment context; identifying a disparity does not by itself explain its cause or decide whether it is legally discriminatory.

Also known as automated decision bias.

Why it matters in practice

Bias analysis examines how design choices and real-world conditions affect different people or groups. It combines quantitative tests with domain understanding and review of the decision process. Teams should document the population, outcome, and limitations studied, because a result on one dataset cannot establish fairness for every use.

Example

A team checks whether a screening model produces different error patterns across groups, then investigates the labels and workflow behind the disparity.

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.