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 scientific articles 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 suggest 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, which fairness desideratum it refers to, how exactly it enables fairness, and who is the stakeholder that benefits from XAI.

翻译：在这项批判性调查中，我们分析了可解释人工智能（XAI）与公平性之间关系的典型论断，以厘清这两个概念的多维关联。基于系统性文献综述和后续的定性内容分析，我们从175篇科学文章中识别出七类关于XAI所谓公平性益处的原型论断。我们针对这些论断提出了关键警示，并为未来探讨XAI在实现特定公平诉求方面的潜力与局限性提供了切入点。重要的是，我们注意到这些论断往往存在以下问题：（i）模糊且简单化，（ii）缺乏规范性基础，或（iii）与XAI的实际能力匹配不足。我们建议将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，而非伦理问题的万能灵药。此外，在提出关于XAI与公平性的论断时，我们强调需要更具体地说明：使用何种XAI方法、涉及何种公平诉求、具体如何促进公平性，以及哪类利益相关者将从XAI中获益。