Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints-猫眼课题宝

DOI：

10.48550/arxiv.2212.12603

发表时间：

2022-12

期刊：

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其他

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作者：

Yao Yao-Yao;Qihang Lin;Tianbao Yang

随着机器学习越来越多地用于做出高风险决策，一个新的挑战是避免不公平的人工智能系统导致受保护人群的歧视性决策。获得公平预测模型的一种直接方法是通过在公平约束下优化其预测性能来训练模型，这在权衡性能与公平性时实现帕累托效率。在各种公平性度量中，基于ROC曲线下面积（AUC）的公平性度量是最近出现的，因为它们与阈值无关并且对不平衡数据有效。在这项工作中，我们将公平感知机器学习模型的训练问题表示为一类基于AUC的公平约束的AUC优化问题。这个问题可以转化为一个最小最大优化问题的最小最大约束，我们解决了随机一阶方法的基础上设计的特殊结构的问题的一个新的Bregman分歧。我们数值上证明了我们的方法在不同的公平性指标下对现实世界的数据的有效性。

As machine learning being used increasingly in making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected population. A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints, which achieves Pareto efficiency when trading off performance against fairness. Among various fairness metrics, the ones based on the area under the ROC curve (AUC) are emerging recently because they are threshold-agnostic and effective for unbalanced data. In this work, we formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. This problem can be reformulated as a min-max optimization problem with min-max constraints, which we solve by stochastic first-order methods based on a new Bregman divergence designed for the special structure of the problem. We numerically demonstrate the effectiveness of our approach on real-world data under different fairness metrics.

Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints

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