论文标题
科学解释和自然语言：统一的认识论语言观点，可解释AI
Scientific Explanation and Natural Language: A Unified Epistemological-Linguistic Perspective for Explainable AI
论文作者
论文摘要
可解释AI（XAI）的基本研究目标是建立能够通过自然语言解释来推理的模型。但是，设计和评估基于解释的推理模型的方法论仍然受到解释性质的理论叙述的了解。为了为XAI提供认识论基础的特征，本文着重于科学领域，旨在弥合理论与实践之间的差距，以科学解释的概念。具体而言，本文结合了对科学哲学哲学中科学解释的现代说明的详细调查，并系统地分析了自然语言解释语料库，从自上而下的（分类）和基于自下而上的（基于科目）的观点阐明了解释性论点的性质和功能。通过定量和定性方法的混合，提出的研究允许得出以下主要结论：（1）不能完全以归纳或演绎论证为主的解释，因为其主要功能是执行统一； （2）必须引用导致事件发生的原因的原因和机制； （3）虽然自然语言解释具有内在的因果力学性质，但它们不仅限于原因和机制，还考虑了务实的元素，例如定义，财产和分类关系； （4）统一模式在解释语料库中自然出现，即使不是故意建模的； （5）通过抽象的过程实现统一，其功能是提供推理基板，以包含在重复模式和高级规律性下进行解释的事件。
A fundamental research goal for Explainable AI (XAI) is to build models that are capable of reasoning through the generation of natural language explanations. However, the methodologies to design and evaluate explanation-based inference models are still poorly informed by theoretical accounts on the nature of explanation. As an attempt to provide an epistemologically grounded characterisation for XAI, this paper focuses on the scientific domain, aiming to bridge the gap between theory and practice on the notion of a scientific explanation. Specifically, the paper combines a detailed survey of the modern accounts of scientific explanation in Philosophy of Science with a systematic analysis of corpora of natural language explanations, clarifying the nature and function of explanatory arguments from both a top-down (categorical) and a bottom-up (corpus-based) perspective. Through a mixture of quantitative and qualitative methodologies, the presented study allows deriving the following main conclusions: (1) Explanations cannot be entirely characterised in terms of inductive or deductive arguments as their main function is to perform unification; (2) An explanation must cite causes and mechanisms that are responsible for the occurrence of the event to be explained; (3) While natural language explanations possess an intrinsic causal-mechanistic nature, they are not limited to causes and mechanisms, also accounting for pragmatic elements such as definitions, properties and taxonomic relations; (4) Patterns of unification naturally emerge in corpora of explanations even if not intentionally modelled; (5) Unification is realised through a process of abstraction, whose function is to provide the inference substrate for subsuming the event to be explained under recurring patterns and high-level regularities.
