Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
2026-06-08 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how complex AI systems can explain their own actions, a concept called Self-Explainability (SX). They reviewed existing research to create a clear definition, categories, and levels for SX. Their findings show that most ideas are still theoretical and that there isn't a standard way to measure how well systems explain themselves. This work aims to guide future research to make AI systems easier to understand and trust.
Self-ExplainabilityExplainable AISelf-Adaptive SystemsSelf-Organising SystemsAI TrustSystem TransparencyEvaluation MethodsTaxonomyLevels of Self-Explainability
Authors
Tom Beyer, Svea Wisy, Sven Tomforde
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 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.