To detect and mitigate societal bias in binary classification, you can use the fairnessMetrics, fairnessWeights, disparateImpactRemover, and fairnessThresholder functions in Statistics and Machine Learning Toolbox™. First, use fairnessMetrics to evaluate the fairness of a data set or classification model using bias and group metrics. Then, use fairnessWeights to reweight observations, disparateImpactRemover to remove the disparate impact of a sensitive attribute, or fairnessThresholder to optimize the classification threshold.

The fairnessWeights and disparateImpactRemover functions provide preprocessing techniques that allow you to adjust your predictor data before training (or retraining) a classifier. The fairnessThresholder function provides a postprocessing technique that adjusts labels near prediction boundaries for a trained classifier. To assess the final model behavior, you can use the fairnessMetrics function as well as various interpretability functions. For more information, see Interpret Machine Learning Models.