A Principles-based Ethics Assurance Argument Pattern for AI and Autonomous Systems
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Abstract (arXiv)
An assurance case is a structured argument, typically produced by safety engineers, to communicate confidence that a critical or complex system, such as an aircraft, will be acceptably safe within its intended context. Assurance cases often inform third party approval of a system. One emerging proposition within the trustworthy AI and autonomous systems (AI/AS) research community is to use assurance cases to instil justified confidence that specific AI/AS will be ethically acceptable when operational in well-defined contexts. This paper substantially develops the proposition and makes it concrete. It brings together the assurance case methodology with a set of ethical principles to structure a principles-based ethics assurance argument pattern. The principles are justice, beneficence, non-maleficence, and respect for human autonomy, with the principle of transparency playing a supporting role. The argument pattern, shortened to the acronym PRAISE, is described. The objective of the proposed PRAISE argument pattern is to provide a reusable template for individual ethics assurance cases, by which engineers, developers, operators, or regulators could justify, communicate, or challenge a claim about the overall ethical acceptability of the use of a specific AI/AS in a given socio-technical context. We apply the pattern to the hypothetical use case of an autonomous robo-taxi service in a city centre.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 11 of its 130 references that are in the map. First posted on arXiv 29 Mar 2022.
Builds on
11 of 130 references.
References · 11
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Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 3.
6 papers.
- 2025Justified Evidence Collection for Argument-based AI Fairness Assurancecited by 2
- 2023Soft Gripping: Specifying for Trustworthinesscited by 0
- 2025CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AIcited by 0
- 2026Safety Case Patterns for VLA-based driving systems: Insights from SimLingocited by 0
- 2026I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contextscited by 0
- 2026An Agentic RAG and Evaluation Framework for Assurance Case Generation: Industrial Use Case for the EU Cyber Resilience Act Compliancecited by 0
- 2026An Agentic RAG and Evaluation Framework for Assurance Case Generation: Industrial Use Case for the EU Cyber Resilience Act Compliance19 Aug 2026
- 2026I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts12 Jun 2026
- 2026Safety Case Patterns for VLA-based driving systems: Insights from SimLingo16 Mar 2026
- 2025CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI14 Sep 2025
- 2025Justified Evidence Collection for Argument-based AI Fairness Assurance12 May 2025
- 2023Soft Gripping: Specifying for Trustworthiness3 Jul 2023