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.

翻译：随着机器学习越来越多地用于高风险决策，一个新兴挑战是避免导致对受保护群体产生歧视性决策的不公平人工智能系统。获得公平预测模型的直接方法是优化模型预测性能的同时满足公平性约束，这在性能与公平性权衡时实现了帕累托效率。在各种公平性指标中，基于ROC曲线下面积（AUC）的指标近年来逐渐兴起，因为它们具有阈值无关性且对不平衡数据有效。本文中，我们将公平感知机器学习模型的训练问题形式化为一个受一类基于AUC公平约束的AUC优化问题。该问题可重构为具有最小-最大约束的最小-最大优化问题，我们基于针对问题特殊结构设计的新型Bregman散度，采用随机一阶方法进行求解。我们在不同公平性指标下的真实数据上数值验证了该方法的有效性。