Explainable AI (xAI) methods are important for establishing trust in using black-box models. However, recent criticism has mounted against current xAI methods that they disagree, are necessarily false, and can be manipulated, which has started to undermine the deployment of black-box models. Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations "must be wrong". However, strict fidelity to the truth is historically not a desideratum in science. Idealizations -- the intentional distortions introduced to scientific theories and models -- are commonplace in the natural sciences and are seen as a successful scientific tool. Thus, it is not falsehood qua falsehood that is the issue. In this paper, I outline the need for xAI research to engage in idealization evaluation. Drawing on the use of idealizations in the natural sciences and philosophy of science, I introduce a novel framework for evaluating whether xAI methods engage in successful idealizations or deceptive explanations (SIDEs). SIDEs evaluates whether the limitations of xAI methods, and the distortions that they introduce, can be part of a successful idealization or are indeed deceptive distortions as critics suggest. I discuss the role that existing research can play in idealization evaluation and where innovation is necessary. Through a qualitative analysis we find that leading feature importance methods and counterfactual explanations are subject to idealization failure and suggest remedies for ameliorating idealization failure.

解释性人工智能（xAI）方法对于建立信任使用黑匣子模型至关重要，本文概述了xAI研究参与理想化评估的需求，并介绍了一个评估xAI方法是否成功理想化或具有欺骗性解释的新框架。我们的定性分析发现，领先的特征重要性方法和反事实解释容易出现理想化失效，并提出了改进理想化失效的方法。

SIDEs: 在xAI中将理想化与欺骗性解释分开