FlipTest: Fairness Auditing via Optimal Transport
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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.
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