Bias Mitigation Post-processing for Individual and Group Fairness
This work addresses fairness issues in critical applications such as employment and criminal justice, though it is incremental as it builds on existing post-processing approaches.
The paper tackled the problem of improving both individual and group fairness in biased classifiers, proposing a post-processing method that outperformed previous work in accuracy, individual fairness, and group fairness on real-world datasets like credit and criminal justice.
Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness. Our novel framework includes an individual bias detector used to prioritize data samples in a bias mitigation algorithm aiming to improve the group fairness measure of disparate impact. We show superior performance to previous work in the combination of classification accuracy, individual fairness and group fairness on several real-world datasets in applications such as credit, employment, and criminal justice.