With the increasing penetration of machine learning applications in critical decision-making areas, calls for algorithmic fairness are more prominent. Although there have been various modalities to improve algorithmic fairness through learning with fairness constraints, their performance does not generalize well in the test set. A performance-promising fair algorithm with better generalizability is needed. This paper proposes a novel adaptive reweighing method to eliminate the impact of the distribution shifts between training and test data on model generalizability. Most previous reweighing methods propose to assign a unified weight for each (sub)group. Rather, our method granularly models the distance from the sample predictions to the decision boundary. Our adaptive reweighing method prioritizes samples closer to the decision boundary and assigns a higher weight to improve the generalizability of fair classifiers. Extensive experiments are performed to validate the generalizability of our adaptive priority reweighing method for accuracy and fairness measures (i.e., equal opportunity, equalized odds, and demographic parity) in tabular benchmarks. We also highlight the performance of our method in improving the fairness of language and vision models. The code is available at https://github.com/che2198/APW.

翻译：随着机器学习应用在关键决策领域的日益渗透，对算法公平性的呼吁愈发突出。尽管已有多种通过公平约束学习来改进算法公平性的方法，但这些方法在测试集上的泛化表现并不理想。因此，亟需一种性能优异且泛化性更强的公平算法。本文提出了一种新颖的自适应重加权方法，以消除训练数据与测试数据之间分布偏移对模型泛化能力的影响。以往的重加权方法通常为每个（子）组分配统一权重，而我们的方法则更精细地建模样本预测结果到决策边界的距离。这种自适应重加权方法优先处理更接近决策边界的样本，并为其分配更高权重，从而提升公平分类器的泛化能力。通过大量实验，我们在表格基准数据集上验证了该方法在准确性和公平性度量（即机会均等、均等化几率及人口统计均等）方面的泛化性能。同时，我们还展示了该方法在改善语言模型和视觉模型公平性方面的表现。代码开源地址：https://github.com/che2198/APW。