# Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency

arXiv: https://arxiv.org/abs/2506.07281 · HTML: https://mlsanity.com/paper/2506.07281/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 77 references that are in the map.
- First posted on arXiv 8 Jun 2025.

## Builds on (most cited first, 18 of 18)

- [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)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs](https://mlsanity.com/paper/2101.09824/) (2021)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [Queer In AI: A Case Study in Community-Led Participatory AI](https://mlsanity.com/paper/2303.16972/) (2023)
- [Participation in the age of foundation models](https://mlsanity.com/paper/2405.19479/) (2024)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [(Un)informed Consent: Studying GDPR Consent Notices in the Field](https://mlsanity.com/paper/1909.02638/) (2019)
- [A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System](https://mlsanity.com/paper/2003.03541/) (2020)
- [Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia](https://mlsanity.com/paper/2402.14147/) (2024)
- [Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems](https://mlsanity.com/paper/2001.04879/) (2020)
- [Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders](https://mlsanity.com/paper/2205.08928/) (2022)
- [Who Followed the Blueprint? Analyzing the Responses of U.S. Federal Agencies to the Blueprint for an AI Bill of Rights](https://mlsanity.com/paper/2404.19076/) (2024)
- [Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms](https://mlsanity.com/paper/2204.13568/) (2022)
- [From Fitting Participation to Forging Relationships: The Art of Participatory ML](https://mlsanity.com/paper/2403.06431/) (2024)
- [Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes](https://mlsanity.com/paper/2303.03367/) (2023)
- [The Dimensions of Data Labor: A Road Map for Researchers, Activists, and Policymakers to Empower Data Producers](https://mlsanity.com/paper/2305.13238/) (2023)

## Built on by (most cited first, 3 of 3)

- [CIRCLE: A Framework for Evaluating AI from a Real-World Lens](https://mlsanity.com/paper/2602.24055/) (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)
- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)

## Abstract (arXiv)

As AI technologies become more human-facing, there have been numerous calls to adapt participatory approaches to AI development -- spurring the idea of participatory AI. However, these calls often focus only on primary stakeholders, such as end-users, and not secondary stakeholders. This paper seeks to translate the ideals of participatory AI to a broader population of secondary AI stakeholders through semi-structured interviews. We theorize that meaningful participation involves three participatory ideals: (1) informedness, (2) consent, and (3) agency. We also explore how secondary stakeholders realize these ideals by traversing a complicated problem space. Like walking up the rungs of a ladder, these ideals build on one another. We introduce three stakeholder archetypes: the reluctant data contributor, the unsupported activist, and the well-intentioned practitioner, who must navigate systemic barriers to achieving agentic AI relationships. We envision an AI future where secondary stakeholders are able to meaningfully participate with the AI systems they influence and are influenced by.