In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature.

中文翻译：

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可解释人工智能 (XAI) 的务实转向

在这篇论文中，我认为，在人工智能中寻找可解释模型和可解释决策的过程必须根据更广泛的项目进行重新表述，即提供对人工智能理解的务实和自然的解释。直观地说，提供模型或决策的解释的目的是使其利益相关者可以理解。但是，如果事先不了解代理理解模型或决策的含义，解释性策略将缺乏明确定义的目标。除了为 XAI 提供更清晰的目标之外，专注于理解还可以让我们放宽解释的事实性条件，这在许多机器学习模型中是不可能实现的，而是专注于确定模型之间最佳拟合的语用条件以及为理解它而部署的方法和设备。在检查了哲学和心理学文献中讨论的不同类型的理解之后，我得出结论，解释或近似模型不仅提供了实现机器学习模型的客观理解的最佳方式，而且还是实现后处理的必要条件。临时可解释性。这一结论部分基于在最近的文献中似乎占主导地位的纯粹功能主义的事后可解释性方法的缺点。但也是实现事后可解释性的必要条件。这一结论部分基于在最近的文献中似乎占主导地位的纯粹功能主义的事后可解释性方法的缺点。但也是实现事后可解释性的必要条件。这一结论部分基于在最近的文献中似乎占主导地位的纯粹功能主义的事后可解释性方法的缺点。