In this critical survey, we analyze typical claims on the relationship between explainable AI (XAI) and fairness to disentangle the multidimensional relationship between these two concepts. Based on a systematic literature review and a subsequent qualitative content analysis, we identify seven archetypal claims from 175 papers on the alleged fairness benefits of XAI. We present crucial caveats with respect to these claims and provide an entry point for future discussions around the potentials and limitations of XAI for specific fairness desiderata. While the literature often suggests XAI to be an enabler for several fairness desiderata, we notice a divide between these desiderata and the capabilities of XAI. We encourage to conceive XAI as one of many tools to approach the multidimensional, sociotechnical challenge of algorithmic fairness and to be more specific about how exactly what kind of XAI method enables whom to address which fairness desideratum.

翻译：在本批判性评述中，我们系统分析了关于可解释人工智能（XAI）与公平性关系的典型论断，旨在厘清这两个概念之间的多维关联。通过系统文献综述及后续定性内容分析，我们从175篇涉及XAI所谓公平性收益的论文中识别出七种典型论断。针对这些论断，我们提出了关键注意事项，并为未来围绕XAI对特定公平性需求的潜力与局限性的讨论提供了切入点。尽管文献常将XAI视为实现多重公平性需求的使能手段，但我们注意到这些需求与XAI实际能力之间存在鸿沟。我们主张应将XAI视为应对算法公平这一多维社会技术挑战的众多工具之一，并更精确地说明何种XAI方法通过何种方式帮助何人实现何种公平性需求。