一篇新论文回顾了复杂人工智能系统中自解释性（SX）的现状和未来研究方向。作者将SX定义为系统解释自身决策的能力，超越了传统的“可解释人工智能”（XAI）。他们的系统性文献综述显示，大多数SX方法仍处于概念阶段，实际应用有限，且没有标准化的评估方法，这表明存在重大的研究空白。 AI
影响 强调了对自解释人工智能系统进行标准化评估和实际应用的需求，这对于复杂人工智能应用中的信任和理解至关重要。
排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了系统性文献综述并提出了未来的研究方向。
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arXiv:2606.09568v1 Announce Type: new Abstract: 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 int…
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 fo…