Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI
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Abstract (arXiv)
Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against a gold standard of Trustworthy AI, consisting of guidelines, requirements, or only expectations. While AI systems are highly complex, their implementations are still based on software. The software engineering community has a long-established toolbox for the assessment of software systems, especially in the context of software testing. In this paper, we argue for the application of software engineering and testing practices for the assessment of trustworthy AI. We make the connection between the seven key requirements as defined by the European Commission's AI high-level expert group and established procedures from software engineering and raise questions for future work.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jul 2020.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineeringcited by 20
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 11
- 2022Towards a Roadmap on Software Engineering for Responsible AIcited by 9
- 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectivescited by 0
- 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives18 Mar 2025
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations12 Jul 2024
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering12 Sep 2022
- 2022Towards a Roadmap on Software Engineering for Responsible AI9 Mar 2022