Using Balancing Terms to Avoid Discrimination in Classification

Abstract

From personalized ad delivery and healthcare to criminal sentencing, more decisions are made with help from methods developed in the fields of data mining and machine learning than ever before. However, their widespread use has raised concerns about the discriminatory impact which the methods may have on people subject to these decisions. Recently, imbalance in the misclassification rates between groups has been identified as a source of discrimination. Such discrimination is not handled by most existing work in discrimination-aware data mining, and it can persist even if other types of discrimination are alleviated. In this article, we present the Balancing Terms (BT) method to address this problem. BT balances the error rates of any classifier with a differentiable prediction function, and unlike existing work, it can incorporate a preference for the trade-off between fairness and accuracy. We empirically evaluate BT on real-world data, demonstrating that our method produces tradeoffs between error rate balance and total classification error that are superior and in only few cases comparable to the state-of-the-art.