Towards a Characterization of Explainable Systems
Not on the map. Why?
Abstract (arXiv)
Building software-driven systems that are easily understood becomes a challenge, with their ever-increasing complexity and autonomy. Accordingly, recent research efforts strive to aid in designing explainable systems. Nevertheless, a common notion of what it takes for a system to be explainable is still missing. To address this problem, we propose a characterization of explainable systems that consolidates existing research. By providing a unified terminology, we lay a basis for the classification of both existing and future research, and the formulation of precise requirements towards such systems.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2021Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligencecited by 13
- 2021On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI Systemcited by 5
- 2022A taxonomy of explanations to support Explainability-by-Designcited by 0
- 2025Context-aware, Ante-hoc Explanations of Driving Behaviourcited by 0
- 2025Context-aware, Ante-hoc Explanations of Driving Behaviour18 Nov 2025
- 2022A taxonomy of explanations to support Explainability-by-Design9 Jun 2022
- 2021Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence29 Dec 2021
- 2021On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System2 Dec 2021