# Dynamic Certification for Autonomous Systems

arXiv: https://arxiv.org/abs/2203.10950 · HTML: https://mlsanity.com/paper/2203.10950/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2022.

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

- [Evaluating Chatbots to Promote Users' Trust -- Practices and Open Problems](https://mlsanity.com/paper/2309.05680/) (2023)
- [Science based AI model certification for untrained operational environments with application in traffic state estimation](https://mlsanity.com/paper/2403.14093/) (2024)
- [Science based AI model certification for new operational environments with application in traffic state estimation](https://mlsanity.com/paper/2405.07893/) (2024)
- [Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI](https://mlsanity.com/paper/2409.00015/) (2024)

## Abstract (arXiv)

Autonomous systems are often deployed in complex sociotechnical environments, such as public roads, where they must behave safely and securely. Unlike many traditionally engineered systems, autonomous systems are expected to behave predictably in varying "open world" environmental contexts that cannot be fully specified formally. As a result, assurance about autonomous systems requires us to develop new certification methods and mathematical tools that can bound the uncertainty engendered by these diverse deployment scenarios, rather than relying on static tools.