Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
This addresses fairness issues in automated decisions for underrepresented groups, offering an incremental improvement over existing bias mitigation techniques.
The paper tackles representation bias in machine learning by proposing an adversarial reweighting method that uses Wasserstein distance to balance data distributions between majority and minority groups, achieving bias mitigation without sacrificing accuracy and outperforming state-of-the-art methods on benchmark datasets.
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.