Systematic Review Examines Self-Explainability in AI-Driven Adaptive Systems
Researchers have published a systematic literature review on Self-Explainability (SX) in self-adaptive and self-organising AI systems, finding that most existing approaches remain conceptual rather than practically implemented. The review introduces a unified definition, taxonomy, and proposed 'Levels of Self-Explainability' framework to structure the field. The findings highlight a significant research gap: there is currently no formal or widely accepted standard for evaluating whether a system can meaningfully explain itself.
A preprint submitted to ACM Transactions on Autonomous and Adaptive Systems presents a systematic literature review examining Self-Explainability (SX) — the capacity of a system to explain its own behaviour and decision-making — in self-adaptive and self-organising systems. The authors distinguish SX from the broader field of Explainable AI (XAI), framing it as a more advanced capability where explanation is intrinsic to the system rather than externally imposed. The review analyses existing approaches across multiple domains, examining their targets and evaluation methods. A key finding is that the overwhelming majority of SX approaches are conceptual, with very few practical implementations documented in the literature. Critically, no formal or de facto standard for evaluating SX currently exists, which the authors identify as a major gap impeding progress. To address this, the paper proposes a unified definition and taxonomy of SX alongside a tiered 'Levels of Self-Explainability' framework intended to help position both current and future research. The work is presented as a foundational roadmap for the field as AI-driven systems grow in complexity and societal deployment.
What's missing
As a preprint under review, this paper has not yet undergone formal peer review, so its taxonomy and framework have not been independently validated. The review's scope, search methodology, inclusion/exclusion criteria, and the number of studies analysed are not described in the abstract, making it difficult to assess comprehensiveness or potential selection bias. It is also unclear how the proposed 'Levels of Self-Explainability' framework was derived or validated empirically.
What different sources said
- arXiv cs.AICenter
Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
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