可解释人工智能的范畴论基础：一种统一理论
Categorical Foundations of Explainable AI: A Unifying Theory
AI总结：
本文利用范畴论首次为可解释人工智能的关键概念（尤其是“解释”）建立数学严格定义，并统一建模学习架构、分类体系与解释方法分析，为XAI奠定可靠理论基础。
摘要：
可解释人工智能（XAI）旨在满足人类对安全可靠AI系统的需求。然而，大量综述强调，XAI关键概念缺乏可靠的数学形式化——尤其值得注意的是，“解释”这一术语至今仍缺乏精确定义。为弥合这一差距，本文利用范畴论这一成熟的形式化工具，首次给出了XAI关键概念与过程的数学严格定义。我们证明，所提出的范畴框架能够：（i）对现有学习方案和架构进行建模，（ii）形式化定义“解释”这一术语，（iii）为XAI分类体系建立理论基础，以及（iv）分析解释方法中常被忽视的方面。因此，我们的范畴框架促进了AI技术的伦理与安全部署，因为它代表着迈向可解释人工智能可靠理论基础的重要一步。
英文摘要：
Explainable AI (XAI) aims to address the human need for safe and reliable AI systems. However, numerous surveys emphasize the absence of a sound mathematical formalization of key XAI notions -- remarkably including the term "explanation" which still lacks a precise definition. To bridge this gap, this paper presents the first mathematically rigorous definitions of key XAI notions and processes, using the well-funded formalism of Category theory. We show that our categorical framework allows to: (i) model existing learning schemes and architectures, (ii) formally define the term "explanation", (iii) establish a theoretical basis for XAI taxonomies, and (iv) analyze commonly overlooked aspects of explaining methods. As a consequence, our categorical framework promotes the ethical and secure deployment of AI technologies as it represents a significant step towards a sound theoretical foundation of explainable AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
