GetFair: Generalized Fairness Tuning of Classification Models

ACM Conference on Fairness, Accountability, and Transparency (FAccT), pp. 289–299

Abstract

We present GetFair, a novel framework for tuning fairness of classification models. The fair classification problem deals with training models for a given classification task where data points have sensitive attributes. The goal of fair classification models is to not only generate accurate classification results but also to prevent discrimination against subpopulations (i.e., individuals with a specific value for the sensitive attribute). Existing methods for enhancing fairness of classification models, however, are often specifically designed for a particular fairness metric or a classifier model. They may also not be suitable for scenarios with incomplete training data or where optimizing for multiple fairness metrics is important. GetFair represents a general solution to this problem.

Authors 3

1. Sandipan Sikdar Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

2. Florian Lemmerich

   University of Passau

   Affiliation as printed

   University of Passau, Germany

3. Markus Strohmaier

   University of Mannheim · GESIS - Leibniz Institute for the Social Sciences

   Affiliation as printed

   University of Mannheim, Germany and GESIS - Leibniz Institute for the Social Sciences, Germany