In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity
of the term "explanation" in explainable AI (XAI) can be solved by adopting any
of four different extant accounts of explanation in the philosophy of science:
the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New
Mechanist models. In this chapter, I show that the authors' claim that these
accounts can be applied to deep neural networks as they would to any natural
phenomenon is mistaken. I also provide a more general argument as to why the
notion of explainability as it is currently used in the XAI literature bears
little resemblance to the traditional concept of scientific explanation. It
would be more fruitful to use the label "understandable AI" to avoid the
confusion that surrounds the goal and purposes of XAI. In the second half of
the chapter, I argue for a pragmatic conception of understanding that is better
suited to play the central role attributed to explanation in XAI. Following
Kuorikoski & Ylikoski (2015), the conditions of satisfaction for understanding
an ML system are fleshed out in terms of an agent's success in using the
system, in drawing correct inferences from it.

深度神经网络的可解释性问题可以通过采用不同的科学解释模型，避免当前解释模糊性而产生的困惑，更有助于用 “可理解的人工智能” 标签避免 XAI 的混淆。