Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we propose Conditional Bias Scan (CBS), a flexible auditing framework for detecting intersectional biases in classification models. CBS identifies the subgroup for which there is the most significant bias against the protected class, as compared to the equivalent subgroup in the non-protected class, and can incorporate multiple commonly used fairness definitions for both probabilistic and binarized predictions. We show that this methodology can detect previously unidentified intersectional and contextual biases in the COMPAS pre-trial risk assessment tool and has higher bias detection power compared to similar methods that audit for subgroup fairness.

翻译：满足受保护群体整体群体公平性标准但不保证子群体公平性的预测模型，可能对处于两个或多个受保护群体交叉点的个体产生有偏预测。为应对这一风险，我们提出条件偏误扫描（CBS），这是一种用于检测分类模型中交叉偏误的灵活审计框架。该框架能够识别出与非受保护群体中等效子群体相比，受保护群体中偏误最显著的子群体，并可整合多种常用的公平性定义（适用于概率预测和二元预测）。实验表明，该方法能够发现COMPAS审前风险评估工具中先前未被识别的交叉偏误和情境偏误，且与类似审计子群体公平性的方法相比，具有更高的偏误检测能力。