通过直接干预分类器性能，实现多群体公平性约束下的精准分类。
Fair Classification by Direct Intervention on Operating Characteristics
- 基于基分类器的群体ROC凸包，直接设定最优运行特征目标。
- 在COMPAS和ACSIncome数据集上同时满足近似DP、EO、PP，干预少且准确率损失小。
- 适用于多保护属性和多种公平约束，适合需要高公平性的实际部署场景。
我们在属性感知设置下，针对二分类任务中的多个群体公平性约束（如人口统计均等性（DP）、等几率（EO）和预测均等性（PP）），开发了新型分类器。提出一种新方法，适用于线性分式约束，通过直接干预预训练基分类器的运行特性：(i) 利用基分类器的群体间ROC凸包识别最优运行特性；(ii) 通过后处理使分类器匹配这些目标。作为实用后处理器，考虑对群体阈值规则的随机混合，以最小化预期干预次数。进一步扩展该方法以处理多个受保护属性和多个线性分式约束。在标准数据集（COMPAS 和 ACSIncome）上，我们的方法能同时满足近似DP、EO和PP，干预次数少，准确率下降接近理想水平，优于以往方法。
原文摘要 · Abstract (English)
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.
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