Assurance 2.0: A Manifesto
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2020.
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Citing papers per year: 2021: 2; 2022: 4.
6 papers.
- 2021Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurancecited by 16
- 2021Safety Case Templates for Autonomous Systemscited by 3
- 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets Approachcited by 3
- 2022Assessing Confidence with Assurance 2.0cited by 3
- 2022Systems Challenges for Trustworthy Embodied Systemscited by 2
- 2022Safe AI -- How is this Possible?cited by 0
- 2022Assessing Confidence with Assurance 2.03 May 2022
- 2022Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach25 Feb 2022
- 2022Safe AI -- How is this Possible?25 Jan 2022
- 2022Systems Challenges for Trustworthy Embodied Systems10 Jan 2022
- 2021Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance30 Nov 2021
- 2021Safety Case Templates for Autonomous Systems29 Jan 2021