首个针对交叉公平性的优化框架，可同时缓解多重偏见。
APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness
- 将交叉公平性建模为多目标优化问题，动态调整优化策略。
- 在4个真实数据集上降低公平性违规，且保持良好准确率。
- 适合关注多重偏见的算法开发者与政策制定者使用。
确保机器学习模型的公平性至关重要，尤其当种族、性别、年龄等保护属性的偏见在交集中叠加时。现有方法仅处理单一属性的公平性，无法捕捉交叉子群体面临的复杂、乘积式偏见。我们提出自适应帕累托前沿探索器（APFEx），首个将交叉公平性明确建模为敏感属性笛卡尔积上的联合优化问题的框架。APFEx包含三项关键创新：(1) 自适应多目标优化器，动态切换帕累托锥投影、梯度加权与探索策略以应对公平性-准确性权衡；(2) 可微分的交叉公平性度量，实现对非光滑子群差异的梯度优化；(3) 收敛至帕累托最优解的理论保证。在四个真实世界数据集上的实验表明，APFEx显著减少公平性违规，同时保持竞争力的准确率。本工作填补了公平机器学习中的关键空白，提供了一种可扩展、模型无关的交叉公平性解决方案。
原文摘要 · Abstract (English)
Ensuring fairness in machine learning models is critical, especially when biases compound across intersecting protected attributes like race, gender, and age. While existing methods address fairness for single attributes, they fail to capture the nuanced, multiplicative biases faced by intersectional subgroups. We introduce Adaptive Pareto Front Explorer (APFEx), the first framework to explicitly model intersectional fairness as a joint optimization problem over the Cartesian product of sensitive attributes. APFEx combines three key innovations- (1) an adaptive multi-objective optimizer that dynamically switches between Pareto cone projection, gradient weighting, and exploration strategies to navigate fairness-accuracy trade-offs, (2) differentiable intersectional fairness metrics enabling gradient-based optimization of non-smooth subgroup disparities, and (3) theoretical guarantees of convergence to Pareto-optimal solutions. Experiments on four real-world datasets demonstrate APFEx's superiority, reducing fairness violations while maintaining competitive accuracy. Our work bridges a critical gap in fair ML, providing a scalable, model-agnostic solution for intersectional fairness.
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