# Assurance 2.0: A Manifesto

arXiv: https://arxiv.org/abs/2004.10474 · HTML: https://mlsanity.com/paper/2004.10474/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance](https://mlsanity.com/paper/2112.00646/) (2021)
- [Safety Case Templates for Autonomous Systems](https://mlsanity.com/paper/2102.02625/) (2021)
- [Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach](https://mlsanity.com/paper/2202.12443/) (2022)
- [Assessing Confidence with Assurance 2.0](https://mlsanity.com/paper/2205.04522/) (2022)
- [Systems Challenges for Trustworthy Embodied Systems](https://mlsanity.com/paper/2201.03413/) (2022)
- [Safe AI -- How is this Possible?](https://mlsanity.com/paper/2201.10436/) (2022)

## Abstract (arXiv)

System assurance is confronted by significant challenges. Some of these are new, for example, autonomous systems with major functions driven by machine learning and AI, and ultra-rapid system development, while others are the familiar, persistent issues of the need for efficient, effective and timely assurance. Traditional assurance is seen as a brake on innovation and often costly and time consuming. We therefore propose a modernized framework, Assurance 2.0, as an enabler that supports innovation and continuous incremental assurance. Perhaps unexpectedly, it does so by making assurance more rigorous, with increased focus on the reasoning and evidence employed, and explicit identification of defeaters and counterevidence.