# An Abstract Architecture for Explainable Autonomy in Hazardous Environments

arXiv: https://arxiv.org/abs/2606.07211 · HTML: https://mlsanity.com/paper/2606.07211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems](https://mlsanity.com/paper/1806.07552/) (2018)
- [Towards Verifiably Ethical Robot Behaviour](https://mlsanity.com/paper/1504.03592/) (2015)
- [Using Formal Methods for Autonomous Systems: Five Recipes for Formal Verification](https://mlsanity.com/paper/2012.00856/) (2020)
- [Heterogeneous Verification of an Autonomous Curiosity Rover](https://mlsanity.com/paper/2007.10045/) (2020)
- [Towards Compositional Verification for Modular Robotic Systems](https://mlsanity.com/paper/2012.01648/) (2020)

## Abstract (arXiv)

Autonomous robotic systems are being proposed for use in hazardous environments, often to reduce the risks to human workers. In the immediate future, it is likely that human workers will continue to use and direct these autonomous robots, much like other computerised tools but with more sophisticated decision-making. Therefore, one important area on which to focus engineering effort is ensuring that these users trust the system. Recent literature suggests that explainability is closely related to how trustworthy a system is. Like safety and security properties, explainability should be designed into a system, instead of being added afterwards. This paper presents an abstract architecture that supports an autonomous system explaining its behaviour (explainable autonomy), providing a design template for implementing explainable autonomous systems. We present a worked example of how our architecture could be applied in the civil nuclear industry, where both workers and regulators need to trust the system's decision-making capabilities.