Bias Mitigation
Techniques applied during AI development to reduce or eliminate biases in models and datasets.
Definition
A suite of interventions—preprocessing (rebalancing or reweighting data), in-processing (fairness-aware learning objectives), and postprocessing (adjusting predictions to meet fairness criteria)—that systematically reduce unwanted disparities. Governance best practices include selecting mitigation strategies aligned to the organization’s risk tolerance and compliance needs.
Real-World Example
A criminal-justice tool that predicts recidivism risk applies a bias-mitigation algorithm: during training, it adds a fairness penalty that reduces prediction gaps between white and Black defendants. After retraining, recidivism prediction rates are statistically equivalent across race groups, and the tool’s deployment guidelines are updated accordingly.
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