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 this https URL

通过提出一种新颖的自适应重新加权方法，该论文旨在消除训练和测试数据之间分布变化对模型普适能力的影响，进而改善算法的公平性和泛化性能。实验证明了该方法在准确性和公平性度量方面的普适性，并突出了对语言和视觉模型公平性改善的性能提升。