# Towards a Characterization of Explainable Systems

arXiv: https://arxiv.org/abs/1902.03096 · HTML: https://mlsanity.com/paper/1902.03096/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021)
- [On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System](https://mlsanity.com/paper/2112.01016/) (2021)
- [A taxonomy of explanations to support Explainability-by-Design](https://mlsanity.com/paper/2206.04438/) (2022)
- [Context-aware, Ante-hoc Explanations of Driving Behaviour](https://mlsanity.com/paper/2511.14428/) (2025)

## 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.