Systematic Review Examines Self-Explainability in AI-Driven Adaptive Systems
A new systematic literature review analyzes approaches to Self-Explainability (SX)—the ability of complex AI systems to explain their own decision-making processes. The review finds that most SX approaches remain largely conceptual with few practical implementations, and identifies the absence of formal evaluation standards as a major research gap. This work is significant because understanding and trusting increasingly complex self-adaptive systems is critical as AI advances.
Researchers have conducted a comprehensive systematic literature review on Self-Explainability in self-adaptive and self-organising systems, examining existing approaches across different domains, targets, and evaluation methods. The study develops a unified definition and taxonomy of SX and introduces a framework called Levels of Self-Explainability to position current and future research efforts. Key findings indicate that while Explainable AI (XAI) aims to provide insight into AI decision-making, Self-Explainability represents a more advanced goal where systems can autonomously explain themselves. The review reveals that most SX approaches remain theoretical or conceptual, with limited practical implementations in real-world systems. Additionally, the research identifies a critical gap: there is currently no formal or de facto standard for evaluating Self-Explainability, which hampers progress in the field. The authors position this work as establishing a foundation and roadmap for advancing Self-Explainability research.
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- arXiv cs.AICenter
Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
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