# Going public: the role of public participation approaches in commercial AI labs

arXiv: https://arxiv.org/abs/2306.09871 · HTML: https://mlsanity.com/paper/2306.09871/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 95 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 19 of 19)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://mlsanity.com/paper/2209.07858/) (2022)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Reading Race: AI Recognises Patient's Racial Identity In Medical Images](https://mlsanity.com/paper/2107.10356/) (2021)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022)
- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [Bringing the People Back In: Contesting Benchmark Machine Learning Datasets](https://mlsanity.com/paper/2007.07399/) (2020)
- [Understanding accountability in algorithmic supply chains](https://mlsanity.com/paper/2304.14749/) (2023)
- [Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics](https://mlsanity.com/paper/2005.07572/) (2020)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [Democratising AI: Multiple Meanings, Goals, and Methods](https://mlsanity.com/paper/2303.12642/) (2023)
- [Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"](https://mlsanity.com/paper/2111.01122/) (2021)
- [What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design](https://mlsanity.com/paper/2007.06718/) (2020)
- [The Limits of Global Inclusion in AI Development](https://mlsanity.com/paper/2102.01265/) (2021)
- [Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making](https://mlsanity.com/paper/2207.09525/) (2022)
- [Towards a New Participatory Approach for Designing Artificial Intelligence and Data-Driven Technologies](https://mlsanity.com/paper/2104.04072/) (2021)

## Built on by (most cited first, 11 of 11)

- [Collective Constitutional AI: Aligning a Language Model with Public Input](https://mlsanity.com/paper/2406.07814/) (2024)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention](https://mlsanity.com/paper/2405.01820/) (2024)
- [Epistemic Injustice in Generative AI](https://mlsanity.com/paper/2408.11441/) (2024)
- [Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms](https://mlsanity.com/paper/2307.10223/) (2023)
- [Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice](https://mlsanity.com/paper/2506.09873/) (2025)
- [Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance](https://mlsanity.com/paper/2408.01458/) (2024)
- [Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participation](https://mlsanity.com/paper/2503.03186/) (2025)
- ["We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research](https://mlsanity.com/paper/2311.18424/) (2023)
- [RelAItionship Building: Analyzing Recruitment Strategies for Participatory AI](https://mlsanity.com/paper/2508.20176/) (2025)

## Abstract (arXiv)

In recent years, discussions of responsible AI practices have seen growing support for "participatory AI" approaches, intended to involve members of the public in the design and development of AI systems. Prior research has identified a lack of standardised methods or approaches for how to use participatory approaches in the AI development process. At present, there is a dearth of evidence on attitudes to and approaches for participation in the sites driving major AI developments: commercial AI labs. Through 12 semi-structured interviews with industry practitioners and subject-matter experts, this paper explores how commercial AI labs understand participatory AI approaches and the obstacles they have faced implementing these practices in the development of AI systems and research. We find that while interviewees view participation as a normative project that helps achieve "societally beneficial" AI systems, practitioners face numerous barriers to embedding participatory approaches in their companies: participation is expensive and resource intensive, it is "atomised" within companies, there is concern about exploitation, there is no incentive to be transparent about its adoption, and it is complicated by a lack of clear context. These barriers result in a piecemeal approach to participation that confers no decision-making power to participants and has little ongoing impact for AI labs. This papers contribution is to provide novel empirical research on the implementation of public participation in commercial AI labs, and shed light on the current challenges of using participatory approaches in this context.