Debiasing Classifiers: Is Reality at Variance With Expectation?

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 classifiers trained 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 optimized. Our results demonstrate that out-of-sample performance on fairness and performance can vary substantially and unexpectedly. More- over, 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.