论文标题
可解释的人工智能（XAI）的务实转弯
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
论文作者
论文摘要
在本文中，我认为，必须根据在AI中提供对务实和自然主义的理解说法的更广泛的项目来重新重新制定AI中的可解释模型和可解释的决策。直观地，提供模型或决定的解释的目的是使其利益相关者可以理解。但是，如果没有以前的掌握，那就是说代理商理解模型或决定的意义，解释性策略将缺乏明确定义的目标。除了为XAI提供更清晰的目标外，专注于理解还可以使我们放宽解释的事实条件，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与方法和设备之间最佳拟合的务实条件，以理解它。在研究了哲学和心理文献中讨论的不同类型的理解之后，我得出结论，解释性或近似模型不仅为实现机器学习模型的对象理解提供了最佳方法，而且还是实现事后解释性的必要条件。该结论部分基于纯粹的功能主义方法的事后解释性方法的缺点，后者在最近的文献中似乎是主要的。
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.
