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. Importantly, we notice that claims are often (i) vague and simplistic, (ii) lacking normative grounding, or (iii) poorly aligned with the actual capabilities of XAI. We encourage to conceive XAI not as an ethical panacea but as one of many tools to approach the multidimensional, sociotechnical challenge of algorithmic fairness. Moreover, when making a claim about XAI and fairness, we emphasize the need to be more specific about what kind of XAI method is used and which fairness desideratum it refers to, how exactly it enables fairness, and who is the stakeholder that benefits from XAI.

翻译：在本批判性综述中，我们分析了可解释人工智能（XAI）与公平性之间典型的主张，以厘清这两个概念的多维关系。基于系统性文献综述及后续的定性内容分析，我们从175篇论文中识别出关于XAI所谓公平性益处的七种典型主张。针对这些主张，我们提出了关键性警示，并为未来围绕XAI在特定公平性需求方面的潜力与局限性的讨论提供了切入点。重要的是，我们注意到这些主张往往（i）模糊且过于简化，（ii）缺乏规范性基础，或（iii）与XAI的实际能力契合度不足。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，而非道德层面的万能药。此外，在提出关于XAI与公平性的主张时，我们强调需要更具体地说明所采用的XAI方法类型及其所指向的公平性需求、该方法如何具体实现公平性，以及受益于XAI的利害关系方是谁。