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.

翻译：样本群体中不同群体的不均衡代表性可能导致机器学习模型在自动决策时对少数群体产生歧视。为解决这些问题，公平感知机器学习联合优化两个（或多个）指标，旨在实现预测有效性与低不公平性。然而，数据中少数群体固有的低代表性使得子群体间的差异对待不易察觉，并在学习过程中难以处理。本文提出一种新颖的对抗性重加权方法来解决这种"表征偏差"。为平衡多数群体与少数群体间的数据分布，我们的方法降低了对多数群体样本的权重。为最小化经验风险，我们的方法优先选择多数群体中与少数群体接近（通过Wasserstein距离评估）的样本。理论分析证明了对抗性重加权方法的有效性。实验表明，我们的方法在不牺牲分类准确率的情况下缓解了偏差，在图像和表格基准数据集上优于相关最先进方法。