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：本综述以批判性视角分析可解释人工智能（XAI）与公平性之间关系的典型论断，旨在厘清这两个概念的多维关联。通过系统性文献综述及后续质性内容分析，我们从175篇论文中识别出七类关于XAI公平性益处的原型主张，并针对这些主张提出关键警示，为未来探讨XAI在特定公平性需求中的潜力与局限性提供切入点。尽管文献常将XAI视为实现多项公平性需求的推动工具，但我们的研究发现这些需求与XAI能力之间存在断层。我们主张将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，并建议更精确地阐明何种XAI方法、通过何种机制、帮助哪些主体实现哪类公平性需求。