Debiasing classifiers: is reality at variance with expectation?

• URL: http://arxiv.org/abs/2011.02407v2
• Date: Mon, 31 May 2021 00:06:53 GMT
• Title: Debiasing classifiers: is reality at variance with expectation?
• Authors: Ashrya Agrawal and Florian Pfisterer and Bernd Bischl and Francois Buet-Golfouse and Srijan Sood and Jiahao Chen and Sameena Shah and Sebastian Vollmer
• Abstract summary: We show that debiasers often fail in practice to generalize out-of-sample data, and can in fact make fairness worse rather than better. Considering fairness--performance trade-offs justifies the counterintuitive notion that partial debiasing can actually yield better results in practice on out-of-sample data.
• Score: 9.730485257882433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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