We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhance fairness. This scheme aims to uphold the sufficiency rule in fairness by ensuring that optimal predictors maintain consistency across diverse sub-groups. We employ a bilevel formulation to address this challenge, wherein we explore sample reweighting strategies. Unlike conventional methods that hinge on model size, our formulation bases generalization complexity on the space of sample weights. We discretize the weights to improve training speed. Empirical validation of our method showcases its effectiveness and robustness, revealing a consistent improvement in the balance between prediction performance and fairness metrics across various experiments.

翻译：本文提出了一种创新方法，通过改进训练数据的重加权方案来增强模型训练中的经验风险最小化（ERM）过程，以提升公平性。该方案旨在通过确保最优预测器在不同子群体间保持一致性，从而维护公平性中的充分性准则。我们采用双层优化公式来解决这一问题，其中探索了样本重加权策略。与依赖模型规模的常规方法不同，我们的公式将泛化复杂度建立在样本权重空间上。我们对权重进行离散化以提高训练速度。本方法的实证验证展示了其有效性和鲁棒性，在各种实验中均显示出预测性能与公平性指标之间平衡的持续改善。