A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups -- and verifying that such fairness has been attained. Because intersectional groups tend to be small, verifying whether a model is fair raises statistical as well as moral-methodological challenges. This paper (1) elucidates the problem of intersectionality in algorithmic fairness, (2) develops desiderata to clarify the challenges underlying the problem and guide the search for potential solutions, (3) illustrates the desiderata and potential solutions by sketching a proposal using simple hypothesis testing, and (4) evaluates, partly empirically, this proposal against the proposed desiderata.

翻译：算法公平性领域一个尚未解决的挑战是交叉性问题，即如何在多个群体的交叉维度上实现公平——并验证这种公平是否已经达成。由于交叉性群体通常规模较小，验证模型是否公平既面临统计上的挑战，也涉及道德-方法论层面的难题。本文（1）阐明算法公平性中的交叉性问题，（2）提出一组需求规范以厘清问题背后的挑战并指导潜在解决方案的探索，（3）通过基于简单假设检验的方案框架说明这些需求规范及潜在解决方案，（4）依据提出的需求规范对该方案进行部分实证评估。