Glossary
Updated
Systematic deviations in AI outputs that unfairly favor or disadvantage particular groups (stemming from skewed datasets, flawed labeling, or mis-specified objectives) and requiring detection, measurement, and mitigation.
Example
A facial-recognition system trained mostly on light-skinned faces shows higher error rates for darker-skinned individuals. The vendor rebalances its training dataset and deploys ongoing bias-monitoring dashboards to ensure equitable performance across all skin tones.