Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions
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 resul...
| Authors | Tom Beyer, Svea Wisy, Sven Tomforde |
|---|---|
| Primary category | cs.AI |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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