Before revision: 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.
After revision: We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and post-processing debiasers for improving fairness in random forest classifierstrained on a suite of data sets.