We present FlipTest, a black-box auditing technique for uncovering subgroup discrimination in predictive models. Combining the concepts of individual and group fairness, we search for discrimination by matching individuals in different protected groups to each other, and their comparing classifier outcomes. Specifically, we formulate a GAN-based approximation of the optimal transport mapping, and use it to translate the distribution of one protected group to that of another, returning pairs of in-distribution samples that statistically correspond to one another. We then define the flipset: the set of individuals whose classifier output changes post-translation, which intuitively corresponds to the set of people who were harmed because of their protected group membership. To shed light on why the model treats a given subgroup differently, we introduce the transparency report: a ranking of features that are most associated with the model's behavior on the flipset. We show that this provides a computationally inexpensive way to identify subgroups that are harmed by model discrimination, including in cases where the model satisfies population-level group fairness criteria.

本研究提出了一种黑盒技术FlipTest，旨在检测分类器中的歧视现象，通过优化传输将不同保护组的个体匹配，创建相似的样本对，构建Flipset以发现歧视，并生成透明度报告以阐明模型对给定子组的行为最相关的特征。

通过最优输运实现公平性测试的FlipTest