mlsanityData to 14 Oct 2026

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

arXiv 2506.07281 ↗First posted 8 Jun 2025cs.HC cs.AIcited by 3

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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.

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

18 of 77 references.

References · 18

  1. 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 138
  2. 2020Participation is not a Design Fix for Machine Learningcited by 111
  3. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 87
  4. 2021Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needscited by 64
  5. 2018Stakeholders in Explainable AIcited by 48
  6. 2023Queer In AI: A Case Study in Community-Led Participatory AIcited by 34
  7. 2024Participation in the age of foundation modelscited by 32
  8. 2021Envisioning Communities: A Participatory Approach Towards AI for Social Goodcited by 30
  9. 2019(Un)informed Consent: Studying GDPR Consent Notices in the Fieldcited by 23
  10. 2020A Human-Centered Review of the Algorithms used within the U.S. Child Welfare Systemcited by 23
  11. 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipediacited by 18
  12. 2020Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systemscited by 16
6 more
  1. 2022Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholderscited by 15
  2. 2024Who Followed the Blueprint? Analyzing the Responses of U.S. Federal Agencies to the Blueprint for an AI Bill of Rightscited by 11
  3. 2022Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harmscited by 10
  4. 2024From Fitting Participation to Forging Relationships: The Art of Participatory MLcited by 8
  5. 2023Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probescited by 6
  6. 2023The Dimensions of Data Labor: A Road Map for Researchers, Activists, and Policymakers to Empower Data Producerscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Three Lessons from Citizen-Centric Participatory AI Design
  2. 2025The Right to AI
  3. 2026Small-world Networks of Agents Brainstorm AI Risks to Support Ideation
  4. 2026Traceable, Enforceable, and Compensable Participation: A Participation Ledger for People-Centered AI Governance
  5. 2026Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory
  6. 2026Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
  7. 2023Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents
  8. 2026Appearing Legitimate is Not Enough: Interrogating Synthetic Agents in Representational Processes through a Participatory Design Lens