# Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice

arXiv: https://arxiv.org/abs/2506.09873 · HTML: https://mlsanity.com/paper/2506.09873/

## 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 (most cited first, 19 of 19)

- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices](https://mlsanity.com/paper/2006.12358/) (2020)
- [The illusion of artificial inclusion](https://mlsanity.com/paper/2401.08572/) (2024)
- [Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems](https://mlsanity.com/paper/2102.04201/) (2021)
- [Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook](https://mlsanity.com/paper/2301.12243/) (2023)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [The Limits of Global Inclusion in AI Development](https://mlsanity.com/paper/2102.01265/) (2021)
- [Designing for Human Rights in AI](https://mlsanity.com/paper/2005.04949/) (2020)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023)
- [Are We Asking the Right Questions?: Designing for Community Stakeholders' Interactions with AI in Policing](https://mlsanity.com/paper/2402.05348/) (2024)
- [Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study](https://mlsanity.com/paper/1906.07946/) (2019)

## Built on by (most cited first, 5 of 5)

- [Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration](https://mlsanity.com/paper/2604.21554/) (2026)
- [To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems](https://mlsanity.com/paper/2604.28053/) (2026)
- [Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation](https://mlsanity.com/paper/2602.18483/) (2026)
- [AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence](https://mlsanity.com/paper/2605.16291/) (2026)
- [Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI](https://mlsanity.com/paper/2607.14353/) (2026)

## 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.