Debiasing classifiers: is reality at variance with expectation?
This highlights critical limitations in fairness interventions for machine learning practitioners, revealing that standard debiasing approaches may be counterproductive in real-world applications.
The study empirically shows that debiasing methods for classifiers often fail to generalize out-of-sample and can worsen fairness, with partial debiasing sometimes yielding better results due to bias-variance trade-offs.
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.