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，算法公平性的呼声愈发突出。尽管已有多种通过公平性约束来改进算法公平性的学习模式，但这些方法在测试集上的泛化性能并不理想。因此，需要一种具有更好泛化能力且性能可期性的公平算法。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之间的分布偏移对模型泛化能力的影响。以往的大多重加权方法为每个（子）组分配统一权重，而我们的方法则对样本预测到决策边界的距离进行精细建模。该方法优先对靠近决策边界的样本进行重加权，赋予更高权重，从而提升公平分类器的泛化能力。我们在表格基准数据集上开展了大量实验，验证了所提出的自适应优先级重加权方法在准确率和公平性指标（即机会均等、赔率均等及人口统计平等）上的泛化能力。同时，我们重点展示了该方法在提升语言和视觉模型公平性方面的性能。代码已开源至 https://github.com/che2198/APW。