Bias Mitigation Techniques: Pre-, In- and Post-Processing

Mitigation methods are often grouped by where they intervene in the machine-learning pipeline. Pre-processing changes the training data or sample weights before a model is fit. Reweighing, for example, changes how instances contribute to learning without necessarily altering the original rows. In-processing changes the learning procedure by adding a fairness constraint, penalty, or adversarial objective. Post-processing changes predicted scores or decisions after a base model is trained, such as choosing thresholds to reduce a selected group disparity. These categories describe mechanics, not guarantees. A method can optimize one fairness definition while worsening another. Demographic parity, equalized odds, and calibration answer different questions and may conflict when base rates differ. The appropriate goal depends on the decision, data, law, and affected people. A threshold adjustment can create different treatment across groups; it may also raise legal or operational questions. Reweighing cannot fix a target that measures an unjust outcome, and adversarially removing group information may not remove correlated proxies. Toolkits implement specific methods. AIF360 provides metrics and pre-, in-, and post-processing algorithms, including Reweighing. Fairlearn includes disaggregated assessment and mitigation techniques such as ExponentiatedGradient and ThresholdOptimizer. ThresholdOptimizer can apply group-specific thresholds under a selected constraint and objective. The method assumes access to sensitive features for fitting or prediction and may use randomized decisions in some configurations. Tools do not choose the legally or socially appropriate fairness criterion for a team. A sound workflow first defines the harm and metric, then establishes a baseline, applies a method, and evaluates on held-out data across relevant groups and intersections. Report accuracy, calibration, uncertainty, and operational consequences alongside fairness metrics. Document which tradeoffs were chosen and who approved them. If no acceptable solution meets safety, validity, and legal needs, do not treat a toolkit result as permission to deploy.