As Machine Learning (ML) models achieve unprecedented levels of performance, the XAI domain aims at making these models understandable by presenting end-users with intelligible explanations. Yet, some existing XAI approaches fail to meet expectations: several issues have been reported in the literature, generally pointing out either technical limitations or misinterpretations by users. In this paper, we argue that the resulting harms arise from a complex overlap of multiple failures in XAI, which existing ad-hoc studies fail to capture. This work therefore advocates for a holistic perspective, presenting a systematic investigation of limitations of current XAI methods and their impact on the interpretation of explanations. By distinguishing between system-specific and user-specific failures, we propose a typological framework that helps revealing the nuanced complexities of explanation failures. Leveraging this typology, we also discuss some research directions to help AI practitioners better understand the limitations of XAI systems and enhance the quality of ML explanations.

机器学习解释的复杂性和局限性对解释的解读产生了多重影响，本研究对现行可解释人工智能方法的局限性进行了系统调查，并提出了一种分类框架来揭示解释失败的复杂性。

为何解释失败？XAI中失败的分类和讨论