The aim of this article is to understand the problem of "black box" algorithms, an issue inherent to the nascent field of Explainable Artificial Intelligence (XAI). While it is relatively easy to understand something someone explained to us, it becomes more complicated when no one can fully grasp the issue. Our purpose is however to highlight: (1) that we should speak of interpretability rather than explainability when we seek to understand models, mainly because we never have complete and unambiguous access to information; (2) that the machines face the problem of the inscrutability of reference, in the same way that the linguist imagined by Willard Van Orman Quine cannot precisely determine what the term "gavagai" refers to in a situation of radical translation; (3) that there is no rule for the application of language, except for "language games", as Ludwig Wittgenstein's linguistics teaches us. The hope of achieving complete explicability and transparency of algorithms is undoubtedly in vain: we can only rely on partial and broad interpretations that will never fully explain the underlying rules.

翻译：本文旨在理解“黑箱”算法这一可解释人工智能（XAI）新兴领域所固有的问题。虽然理解他人向我们解释的事物相对容易，但当无人能完全把握问题时，情况就变得复杂。然而，我们的目的是强调：（1）在试图理解模型时，我们应谈论可解释性（interpretability）而非可说明性（explainability），主要原因是我们永远无法获得完整且无歧义的信息；（2）机器面临着指称难解性问题，这与威拉德·范·奥曼·奎因所设想的语言学家在“彻底翻译”情境中无法精确确定“gavagai”一词所指的情况类似；（3）正如路德维希·维特根斯坦的语言学教导我们的，不存在应用语言的规则，只有“语言游戏”。期望实现算法的完全可说明性与透明性无疑是徒劳的：我们只能依赖部分且宽泛的解释，这些解释永远无法完全阐明其底层规则。