We develop new classifiers under group fairness in the attribute-aware setting for binary classification with multiple group fairness constraints (e.g., demographic parity (DP), equalized odds (EO), and predictive parity (PP)). We propose a novel approach, applicable to linear fractional constraints, based on directly intervening on the operating characteristics of a pre-trained base classifier, by (i) identifying optimal operating characteristics using the base classifier's group-wise ROC convex hulls and (ii) post-processing the base classifier to match those targets. As practical post-processors, we consider randomizing a mixture of group-wise thresholding rules subject to minimizing the expected number of interventions. We further extend our approach to handle multiple protected attributes and multiple linear fractional constraints. On standard datasets (COMPAS and ACSIncome), our methods simultaneously satisfy approximate DP, EO, and PP with few interventions and a near-oracle drop in accuracy; comparing favorably to previous methods.

翻译：本文针对具有多重群体公平约束（如人口统计均等（DP）、均等化机会（EO）和预测均等（PP））的二元分类问题，在属性感知场景下开发了新的群体公平分类器。我们提出了一种适用于线性分数约束的新颖方法，其核心在于直接干预预训练基分类器的操作特性，具体通过以下步骤实现：（i）利用基分类器的组别ROC凸包识别最优操作特性；（ii）对基分类器进行后处理以匹配这些目标。作为实际的后处理器，我们考虑在最小化预期干预次数的前提下，对组别阈值规则的混合策略进行随机化处理。我们进一步扩展了该方法以处理多个受保护属性和多重线性分数约束。在标准数据集（COMPAS和ACSIncome）上的实验表明，我们的方法能以较少的干预次数和接近最优的精度损失，同时满足近似DP、EO和PP约束，其性能优于现有方法。