# Self-Explainability in Self-Adaptive and Self-Organising Systems: Status and Research Directions

arXiv: https://arxiv.org/abs/2606.09568 · HTML: https://mlsanity.com/paper/2606.09568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 169 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 28 of 28)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations](https://mlsanity.com/paper/1912.09007/) (2019)
- [Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement](https://mlsanity.com/paper/2203.08008/) (2022)
- [This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation](https://mlsanity.com/paper/2108.12204/) (2021)
- [Explainability Pitfalls: Beyond Dark Patterns in Explainable AI](https://mlsanity.com/paper/2109.12480/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
- [Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2212.10693/) (2022)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Embedding Deep Networks into Visual Explanations](https://mlsanity.com/paper/1709.05360/) (2017)
- [Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification](https://mlsanity.com/paper/2209.14974/) (2022)
- [Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework](https://mlsanity.com/paper/2311.11055/) (2023)
- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
- [A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading](https://mlsanity.com/paper/1712.08107/) (2017)
- [Explainable Natural Language Processing for Corporate Sustainability Analysis](https://mlsanity.com/paper/2407.17487/) (2024)
- [Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features](https://mlsanity.com/paper/2408.17198/) (2024)
- [Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains](https://mlsanity.com/paper/2101.00125/) (2021)
- [LLMs are One-Shot URL Classifiers and Explainers](https://mlsanity.com/paper/2409.14306/) (2024)
- [Towards Self-Explainable Cyber-Physical Systems](https://mlsanity.com/paper/1908.04698/) (2019)
- [Organic Computing in the Spotlight](https://mlsanity.com/paper/1701.08125/) (2017)
- [SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach](https://mlsanity.com/paper/2402.03043/) (2024)
- [Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction](https://mlsanity.com/paper/2110.04598/) (2021)
- [Generating Explanations for Autonomous Robots: a Systematic Review](https://mlsanity.com/paper/2412.18516/) (2024)
- [Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics](https://mlsanity.com/paper/2405.02334/) (2024)
- [Interpretable classification of wiki-review streams](https://mlsanity.com/paper/2405.18335/) (2024)

## Abstract (arXiv)

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.