Fairness Auditing

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Fairness auditing looks at whether a model produces consistent outcomes for different groups in the situation where it’ll actually be used. It goes beyond overall accuracy and examines how results vary, since a system can perform well on average while still causing harm to specific populations. By comparing outcomes such as error rates or decisions made, teams can identify patterns that point to unequal treatment.

In real projects, what counts as “fair” depends on the goal and the context, so there is no single metric that works everywhere. Teams select measures that fit the decision being made and the risks involved. The findings are documented so they can guide concrete changes, such as improving data coverage or adjusting how results are interpreted. While software tools can support this process, deciding what is acceptable and what needs fixing ultimately requires human judgment.

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