Why do explanations fail? A typology and discussion on failures in XAI
CoRR(2024)
摘要
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.
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