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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 …

Debiasing Through a Causal Lens

Recent research has shown, that debiasing methods often do not reliably increase fairness in practical applications while simultaneously decreasing a model's accuracy. We study the effect of debiasing methods through a causal lens in order to develop …