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，对算法公平性的呼声愈加凸显。尽管已有多种通过公平性约束改进算法公平性的方法，但其性能在测试集上泛化效果不佳。亟需一种兼具良好泛化能力且性能优异的公平算法。本文提出一种新颖的自适应重加权方法，旨在消除训练数据与测试数据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）组赋予统一权重，而我们的方法则从细粒度层面建模样本预测值到决策边界的距离。这种自适应重加权方法优先关注距离决策边界较近的样本，并赋予其更高权重，从而提升公平分类器的泛化能力。我们在表格数据集上进行了大量实验，验证了所提出的自适应优先级重加权方法在准确率与公平性度量（即机会均等、均等化优势及人口统计均等）中的泛化性能。此外，我们还重点展示了该方法在提升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。