Assessing Confidence with Assurance 2.0
Not on the map. Why?
Abstract (arXiv)
An assurance case is intended to provide justifiable confidence in the truth of its top claim, which typically concerns safety or security. A natural question is then "how much" confidence does the case provide? We argue that confidence cannot be reduced to a single attribute or measurement. Instead, we suggest it should be based on attributes that draw on three different perspectives: positive, negative, and residual doubts. Positive Perspectives consider the extent to which the evidence and overall argument of the case combine to make a positive statement justifying belief in its claims. We set a high bar for justification, requiring it to be indefeasible. The primary positive measure for this is soundness, which interprets the argument as a logical proof. Confidence in evidence can be expressed probabilistically and we use confirmation measures to ensure that the "weight" of evidence crosses some threshold. In addition, probabilities can be aggregated from evidence through the steps of the argument using probability logics to yield what we call probabilistic valuations for the claims. Negative Perspectives record doubts and challenges to the case, typically expressed as defeaters, and their exploration and resolution. Assurance developers must guard against confirmation bias and should vigorously explore potential defeaters as they develop the case, and should record them and their resolution to avoid rework and to aid reviewers. Residual Doubts: the world is uncertain so not all potential defeaters can be resolved. We explore risks and may deem them acceptable or unavoidable. It is crucial however that these judgments are conscious ones and that they are recorded in the assurance case. This report examines the perspectives in detail and indicates how Clarissa, our prototype toolset for Assurance 2.0, assists in their evaluation.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 5 of its 108 references that are in the map. First posted on arXiv 3 May 2022.
Builds on
5 of 108 references.
References · 5
- 2017Thinking Fast and Slow: Optimization Decomposition Across Timescalescited by 60
- 2019Assessing the Safety and Reliability of Autonomous Vehicles from Road Testingcited by 15
- 2020Assurance 2.0: A Manifestocited by 6
- 2021Bootstrapping confidence in future safety based on past safe operationcited by 2
- 2022DesCert: Design for Certificationcited by 1
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2024Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentationcited by 1
- 2026Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scalecited by 1
- 2025OntoGSN: An Ontology-Based Framework for Semantic Management and Extension of Assurance Casescited by 0
- 2026Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale15 Feb 2026
- 2025OntoGSN: An Ontology-Based Framework for Semantic Management and Extension of Assurance Cases20 May 2025
- 2024Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation10 Oct 2024