让复杂系统自己解释决策，提升可理解性与信任度。
Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
- 提出自解释能力统一定义与分级框架
- 发现现有研究多为概念层面，缺乏实际落地
- 为未来研究提供方向与评估标准
随着人工智能技术的发展，自适应与自组织系统的复杂性不断上升，使其越来越难以理解与信赖。尽管可解释AI旨在揭示AI决策过程，更进一步的目标是让系统具备自我解释能力，即自解释性（Self-Explainability, SX）。本文开展系统性文献综述，分析现有方法的领域、目标与评估方式，提出统一定义与分类体系，并构建自解释性层级框架，用于定位当前及未来研究。结果表明，多数SX方法仍停留在概念阶段，实践应用极少；同时，目前尚无正式或事实上的评估标准，存在显著研究空白。本工作为此类复杂系统中的自解释性发展奠定了基础并指明了路线图。
原文摘要 · Abstract (English)
The growing complexity of self-adaptive and self-organising systems, fuelled by advances in Artificial Intelligence (AI), has made them increasingly difficult to understand and trust. While Explainable AI aims to provide insight into AI decision-making, a more advanced goal is for systems to explain themselves - an ability referred to as Self-Explainability (SX). This article presents a systematic literature review on SX, analysing existing approaches, including their domains, targets, and evaluation methods. The review develops a unified definition and taxonomy of SX and introduces Levels of Self-Explainability, providing a framework for positioning current and future research. Our results show that most SX approaches remain conceptual, with few practical implementations. Moreover, there is currently no formal or de facto standard for evaluating SX, highlighting a major research gap. This work thus establishes a foundation and roadmap for advancing Self-Explainability in complex systems.
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