通过可调参数控制公平性与准确率的权衡，减少社会属性偏差。
A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities
- 用代理损失函数和重加权技术实现公平性调控
- 在多个数据集上提升准确率同时降低公平性违规
- 适合需要灵活平衡公平与性能的研究者
本文提出一种名为 α-β 鲁棒机器学习（α-β FML）的新算法公平性框架，旨在优化不同社会人口属性下的公平性水平。该框架采用新型代理损失函数族，并结合损失重加权技术，通过可调超参数 α 与 β 精确控制公平性与准确率之间的权衡。为高效求解学习目标，我们提出了带代理损失的并行随机梯度下降（P-SGD-S），并建立了凸与非凸损失函数下的收敛保证。实验结果表明，该框架在多个数据集上均提升了整体准确率，同时减少了公平性违规，实现了标准经验风险最小化与严格极小极大公平性之间的平滑过渡，具备良好的适应性，确保公平性改进不伴随过度性能下降。
原文摘要 · Abstract (English)
This paper presents a new algorithmic fairness framework called $\boldsymbolα$-$\boldsymbolβ$ Fair Machine Learning ($\boldsymbolα$-$\boldsymbolβ$ FML), designed to optimize fairness levels across sociodemographic attributes. Our framework employs a new family of surrogate loss functions, paired with loss reweighting techniques, allowing precise control over fairness-accuracy trade-offs through tunable hyperparameters $\boldsymbolα$ and $\boldsymbolβ$. To efficiently solve the learning objective, we propose Parallel Stochastic Gradient Descent with Surrogate Loss (P-SGD-S) and establish convergence guarantees for both convex and nonconvex loss functions. Experimental results demonstrate that our framework improves overall accuracy while reducing fairness violations, offering a smooth trade-off between standard empirical risk minimization and strict minimax fairness. Results across multiple datasets confirm its adaptability, ensuring fairness improvements without excessive performance degradation.
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