  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.
