  The unequal representation of different groups in a sample population can
lead to discrimination of minority groups when machine learning models make
automated decisions. To address these issues, fairness-aware machine learning
jointly optimizes two (or more) metrics aiming at predictive effectiveness and
low unfairness. However, the inherent under-representation of minorities in the
data makes the disparate treatment of subpopulations less noticeable and
difficult to deal with during learning. In this paper, we propose a novel
adversarial reweighting method to address such \emph{representation bias}. To
balance the data distribution between the majority and the minority groups, our
approach deemphasizes samples from the majority group. To minimize empirical
risk, our method prefers samples from the majority group that are close to the
minority group as evaluated by the Wasserstein distance. Our theoretical
analysis shows the effectiveness of our adversarial reweighting approach.
Experiments demonstrate that our approach mitigates bias without sacrificing
classification accuracy, outperforming related state-of-the-art methods on
image and tabular benchmark datasets.

Machine Learning