Intersectionality is a critical framework that, through inquiry and praxis, allows us to examine how social inequalities persist through domains of structure and discipline. Given AI fairness' raison d'etre of "fairness", we argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness. Through a critical review of how intersectionality is discussed in 30 papers from the AI fairness literature, we deductively and inductively: 1) map how intersectionality tenets operate within the AI fairness paradigm and 2) uncover gaps between the conceptualization and operationalization of intersectionality. We find that researchers overwhelmingly reduce intersectionality to optimizing for fairness metrics over demographic subgroups. They also fail to discuss their social context and when mentioning power, they mostly situate it only within the AI pipeline. We: 3) outline and assess the implications of these gaps for critical inquiry and praxis, and 4) provide actionable recommendations for AI fairness researchers to engage with intersectionality in their work by grounding it in AI epistemology.

翻译：交叉性是一个关键框架，通过探究与实践，使我们能够审视社会不平等如何在结构与规训的领域中持续存在。鉴于人工智能公平的核心理念是“公平”，我们认为采用交叉性作为分析框架对于有效实现公平至关重要。通过对人工智能公平文献中30篇论文关于交叉性讨论的批判性审视，我们采用演绎与归纳的方法：1）描绘交叉性原则在人工智能公平范式中的运作方式；2）揭示交叉性概念化与操作化之间的差距。我们发现研究者大多将交叉性简化为针对人口统计子群体优化公平指标。他们还未能探讨自身的社会背景，且在提及权力时，主要将其局限于人工智能管道的范围内。我们：3）概述并评估这些差距对批判性探究与实践的影响；4）为人工智能公平研究者提供切实可行的建议，通过将交叉性根植于人工智能认识论中，使其融入研究工作。