破解算法公平性中的交叉歧视难题，让少数群体不被忽视。
The Intersectionality Problem for Algorithmic Fairness
- 提出交叉公平性问题的系统性分析框架
- 设计可验证公平性的评估标准与测试方法
- 适合关注算法伦理与公平性的研究者阅读
算法公平性面临尚未解决的交叉性难题，即在多个群体交集上实现并验证公平性。由于交叉群体规模通常较小，验证模型公平性既面临统计挑战，也涉及道德与方法论难题。本文（1）阐明交叉性问题的本质，（2）提出理想准则以厘清挑战并指导解决方案探索，（3）通过简单假设检验的方法示例说明准则与潜在解法，（4）部分基于实证对所提方案在理想准则下的表现进行评估。
原文摘要 · Abstract (English)
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.
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