Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice
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Abstract (arXiv)
Responsible AI (rAI) guidance increasingly promotes stakeholder involvement (SHI) during AI development. At the same time, SHI is already common in commercial software development, but with potentially different foci. This study clarifies the extent to which established SHI practices are able to contribute to rAI efforts as well as potential disconnects -- essential insights to inform and tailor future interventions that further shift industry practice towards rAI efforts. First, we analysed 56 rAI guidance documents to identify why SHI is recommended (i.e. its expected benefits for rAI) and uncovered goals such as redistributing power, improving socio-technical understandings, anticipating risks, and enhancing public oversight. To understand why and how SHI is currently practised in commercial settings, we then conducted an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. Our findings reveal that SHI in practice is primarily driven by commercial priorities (e.g. customer value, compliance) and several factors currently discourage more rAI-aligned SHI practices. This suggests that established SHI practices are largely not contributing to rAI efforts. To address this disconnect, we propose interventions and research opportunities to advance rAI development in practice.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 19 of its 120 references that are in the map. First posted on arXiv 11 Jun 2025.
Builds on
19 of 120 references.
References · 19
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7 more
- 2022Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineeringcited by 20
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Built on by
Citing papers per year: 2026: 5.
5 papers.
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- 2026Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AIcited by 0
- 2026Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI15 Jul 2026
- 2026To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems30 Apr 2026
- 2026Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration23 Apr 2026
- 2026AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence14 Apr 2026
- 2026Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation10 Feb 2026