Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns
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Abstract (arXiv)
Although artificial intelligence (AI) is solving real-world challenges and transforming industries, there are serious concerns about its ability to behave and make decisions in a responsible way. Many AI ethics principles and guidelines for responsible AI have been recently issued by governments, organisations, and enterprises. However, these AI ethics principles and guidelines are typically high-level and do not provide concrete guidance on how to design and develop responsible AI systems. To address this shortcoming, we first present an empirical study where we interviewed 21 scientists and engineers to understand the practitioners' perceptions on AI ethics principles and their implementation. We then propose a template that enables AI ethics principles to be operationalised in the form of concrete patterns and suggest a list of patterns using the newly created template. These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 71 references that are in the map. First posted on arXiv 18 Nov 2021.
Builds on
14 of 71 references.
References · 14
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- 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
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- 2018FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformitycited by 31
- 2021Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Makingcited by 23
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- 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
2 more
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Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 0; 2025: 1; 2026: 2.
9 papers.
- 2021AI Ethics Principles in Practice: Perspectives of Designers and Developerscited by 12
- 2022Towards a Roadmap on Software Engineering for Responsible AIcited by 9
- 2022Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systemscited by 4
- 2023Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repositorycited by 4
- 2023Responsible Design Patterns for Machine Learning Pipelinescited by 4
- 2025Toward Agentic Software Engineering Beyond Code: Framing Vision, Values, and Vocabularycited by 4
- 2026Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scalecited by 1
- 2023Distributed Trust Through the Lens of Software Architecturecited by 0
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research11 Aug 2026
- 2026Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale15 Feb 2026
- 2025Toward Agentic Software Engineering Beyond Code: Framing Vision, Values, and Vocabulary22 Oct 2025
- 2023Responsible Design Patterns for Machine Learning Pipelines31 May 2023
- 2023Distributed Trust Through the Lens of Software Architecture25 May 2023
- 2023Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository23 Mar 2023
- 2022Towards a Roadmap on Software Engineering for Responsible AI9 Mar 2022
- 2022Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems2 Mar 2022
- 2021AI Ethics Principles in Practice: Perspectives of Designers and Developers14 Dec 2021