RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Roles
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Abstract (arXiv)
Many guidelines for responsible AI have been suggested to help AI practitioners in the development of ethical and responsible AI systems. However, these guidelines are often neither grounded in regulation nor usable by different roles, from developers to decision makers. To bridge this gap, we developed a four-step method to generate a list of responsible AI guidelines; these steps are: (1) manual coding of 17 papers on responsible AI; (2) compiling an initial catalog of responsible AI guidelines; (3) refining the catalog through interviews and expert panels; and (4) finalizing the catalog. To evaluate the resulting 22 guidelines, we incorporated them into an interactive tool and assessed them in a user study with 14 AI researchers, engineers, designers, and managers from a large technology company. Through interviews with these practitioners, we found that the guidelines were grounded in current regulations and usable across roles, encouraging self-reflection on ethical considerations at early stages of development. This significantly contributes to the concept of `Responsible AI by Design' -- a design-first approach that embeds responsible AI values throughout the development lifecycle and across various business roles.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jul 2023.
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Citing papers per year: 2024: 1; 2025: 6; 2026: 2.
9 papers.
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Expertscited by 10
- 2025Characterizing LLM-driven Social Network: The Chirper.ai Casecited by 9
- 2025Understanding Gender Bias in AI-Generated Product Descriptionscited by 3
- 2025The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platformcited by 2
- 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stagescited by 1
- 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systemscited by 1
- 2025Can AI be Accountable?cited by 0
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0
- 2026Designing the Future of User Feedback for Generative AIcited by 0
- 2026Designing the Future of User Feedback for Generative AI2 Oct 2026
- 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research11 Aug 2026
- 2025Can AI be Accountable?30 Oct 2025
- 2025Multi-Agent LLMs as Ethics Advocates for AI-Based Systems11 Jul 2025
- 2025Understanding Gender Bias in AI-Generated Product Descriptions3 Jun 2025
- 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages15 May 2025
- 2025Characterizing LLM-driven Social Network: The Chirper.ai Case14 Apr 2025
- 2025The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platform8 Apr 2025
- 2024Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts24 Jul 2024