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Assurance 2.0: A Manifesto
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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.
Forward citations
Cited by 3 Pith papers
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The Impact of Operational-Data Fidelity when Assessing Safety-Critical Autonomous-Vehicle Software
Coarse failure data without false-positive vs false-negative labels can make conservative Bayesian reliability claims for AV software dangerously optimistic, sometimes infinitely so after a single failure.
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Assessing confidence in frontier AI safety cases
Applying Assurance 2.0 to a cyber-misuse safety case, the authors show that high top-level confidence requires extremely high confidence in every component, and propose an LLM-based Delphi for eliciting those componen...
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Where AI Assurance Might Go Wrong: Initial lessons from engineering of critical systems
A position paper mapping traditional critical systems engineering to AI safety frameworks and advocating Assurance 2.0-style cases, broader boundaries, and explicit risk tolerability.
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