To detect and mitigate societal bias in binary classification, you can use the fairnessMetrics, fairnessWeights, and disparateImpactRemover 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, or use disparateImpactRemover to remove the disparate impact of a sensitive attribute.

The fairnessWeights and disparateImpactRemover functions provide preprocessing techniques that allow you to adjust your predictor data before training (or retraining) a classifier. To assess the model behavior after training, you can use the fairnessMetrics function as well as various interpretability functions. For more information, see Interpret Machine Learning Models.