Many methods for debiasing classifiers have been proposed, but their effectiveness in practice remains unclear. We evaluate the performance of pre-processing and post-processing debiasers for improving fairness in random forest classifierstrained on a suite of data sets. Specifically, we study how these debiasers generalize with respect to both out-of-sample test error for computing fairness -- performance and fairness -- fairness trade-offs, and on the change in other fairness metrics that were not explicitly optimised. Our results demonstrate that out-of-sample performance on fairness and performance can vary substantially and unexpectedly. Moreover, the variance in estimation arises from class imbalances with respect to both the outcome and the protected classes. Our results highlight the importance of evaluating out-of-sample performance in practical usage .
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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Many methods for debiasing classifiers have been proposed, but their effectiveness in practice remains unclear. We evaluate the performance of pre-processing
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample,
R
post-processing debiasers for improving fairness in random forest classifierstrained on a suite of data sets. Specifically, we study how these debiasers generalize with respect to both
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
R
test error for computing fairness -- performance and fairness -- fairness trade-offs, and on the change in other fairness metrics that were not explicitly optimised. Our results demonstrate that out-of-sample performance on fairness and performance can vary substantially and unexpectedly. Moreover, the variance in estimation arises from class imbalances with respect to both the outcome and the protected classes. Our results highlight the importance of evaluating out-of-sample performance in practical usage
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