We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse rather than better. A rigorous evaluation of the debiasing treatment effect requires extensive cross-validation beyond what is usually done. We demonstrate that this phenomenon can be explained as a consequence of bias-variance trade-off, with an increase in variance necessitated by imposing a fairness constraint. Follow-up experiments validate the theoretical prediction that the estimation variance depends strongly on the base rates of the protected class. Considering fairness--performance trade-offs justifies the counterintuitive notion that partial debiasing can actually yield better results in practice on out-of-sample data.
@article{arxiv.2011.02407,
title = {Debiasing classifiers: is reality at variance with expectation?},
author = {Ashrya Agrawal and Florian Pfisterer and Bernd Bischl and Francois Buet-Golfouse and Srijan Sood and Jiahao Chen and Sameena Shah and Sebastian Vollmer},
journal= {arXiv preprint arXiv:2011.02407},
year = {2021}
}