Explanatory AI (XAI) is on the rise, gaining enormous traction with the computational community, policymakers, and philosophers alike. This article contributes to this debate by first distinguishing scientific XAI (sXAI) from other forms of XAI. It further advances the structure for bona fide sXAI, while remaining neutral regarding preferences for theories of explanations. Three core components are under study, namely, i) the structure for bona fide sXAI, consisting in elucidating the explanans, the explanandum, and the explanatory relation for sXAI: ii) the pragmatics of explanation, which includes a discussion of the role of multi-agents receiving an explanation and the context within which the explanation is given; and iii) a discussion on Meaningful Human Explanation, an umbrella concept for different metrics required for measuring the explanatory power of explanations and the involvement of human agents in sXAI. The kind of AI systems of interest in this article are those utilized in medicine and the healthcare system. The article also critically addresses current philosophical and computational approaches to XAI. Amongst the main objections, it argues that there has been a long-standing interpretation of classifications as explanation, when these should be kept separate.

解释性AI（XAI）呈上升趋势，在计算社区，政策制定者和哲学家中都获得了极大的关注。本文通过首先将科学XAI（sXAI）与其他形式的XAI区别开来，为这场辩论做出了贡献。它进一步改进了真正的sXAI的结构，同时在解释理论的偏好方面保持中立。正在研究三个核心组成部分，即：i）善意sXAI的结构，包括阐明explanans，explanandum以及解释性关系对于sXAI：ii）解释的语用，其中包括讨论接受解释的多主体的角色以及给出解释的上下文；iii）关于“有意义的人类解释”的讨论，“有意义的人类解释”是衡量度量的解释力和人类行为者参与sXAI所需的不同度量的总体概念。本文关注的AI系统类型是那些在医学和医疗保健系统中使用的AI系统。本文还批判性地介绍了当前XAI的哲学和计算方法。在主要反对意见中，它主张对分类进行长期的解释作为解释，何时应将它们分开。