# From Fitting Participation to Forging Relationships: The Art of Participatory ML

arXiv: https://arxiv.org/abs/2403.06431 · HTML: https://mlsanity.com/paper/2403.06431/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2024.

## Built on by (most cited first, 8 of 8)

- [Acceptable Use Policies for Foundation Models](https://mlsanity.com/paper/2409.09041/) (2024)
- [Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships](https://mlsanity.com/paper/2407.12543/) (2024)
- [Digital Companionship: Overlapping Uses of AI Companions and AI Assistants](https://mlsanity.com/paper/2510.15905/) (2025)
- [Attention to Non-Adopters](https://mlsanity.com/paper/2510.15951/) (2025)
- [Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participation](https://mlsanity.com/paper/2503.03186/) (2025)
- [Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency](https://mlsanity.com/paper/2506.07281/) (2025)
- [RelAItionship Building: Analyzing Recruitment Strategies for Participatory AI](https://mlsanity.com/paper/2508.20176/) (2025)
- [Designing the Future of User Feedback for Generative AI](https://mlsanity.com/paper/2610.02631/) (2026)

## Abstract (arXiv)

Participatory machine learning (ML) encourages the inclusion of end users and people affected by ML systems in design and development processes. We interviewed 18 participation brokers -- individuals who facilitate such inclusion and transform the products of participants' labour into inputs for an ML artefact or system -- across a range of organisational settings and project locations. Our findings demonstrate the inherent challenges of integrating messy contextual information generated through participation with the structured data formats required by ML workflows and the uneven power dynamics in project contexts. We advocate for evolution in the role of brokers to more equitably balance value generated in Participatory ML projects for design and development teams with value created for participants. To move beyond `fitting' participation to existing processes and empower participants to envision alternative futures through ML, brokers must become educators and advocates for end users, while attending to frustration and dissent from indirect stakeholders.