# RelAItionship Building: Analyzing Recruitment Strategies for Participatory AI

arXiv: https://arxiv.org/abs/2508.20176 · HTML: https://mlsanity.com/paper/2508.20176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 89 references that are in the map.
- First posted on arXiv 27 Aug 2025.

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

- [Fine-tuning language models to find agreement among humans with diverse preferences](https://mlsanity.com/paper/2211.15006/) (2022)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (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)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- ["The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning](https://mlsanity.com/paper/1911.08089/) (2019)
- [Adapting a Kidney Exchange Algorithm to Align with Human Values](https://mlsanity.com/paper/2005.09755/) (2020)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems](https://mlsanity.com/paper/2102.01196/) (2021)
- [Enabling Value Sensitive AI Systems through Participatory Design Fictions](https://mlsanity.com/paper/1912.07381/) (2019)
- [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023)
- [Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness](https://mlsanity.com/paper/2009.09559/) (2020)
- [An Uncommon Task: Participatory Design in Legal AI](https://mlsanity.com/paper/2203.06246/) (2022)
- [From Fitting Participation to Forging Relationships: The Art of Participatory ML](https://mlsanity.com/paper/2403.06431/) (2024)
- [Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence](https://mlsanity.com/paper/2004.06675/) (2020)
- [Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?](https://mlsanity.com/paper/2304.02134/) (2023)
- [What Makes An Expert? Reviewing How ML Researchers Define "Expert"](https://mlsanity.com/paper/2411.00179/) (2024)
- ["Come to us first": Centering Community Organizations in Artificial Intelligence for Social Good Partnerships](https://mlsanity.com/paper/2409.06814/) (2024)

## Abstract (arXiv)

Participatory AI, in which impacted community members and other stakeholders are involved in the design and development of AI systems, holds promise as a way to ensure AI is developed to meet their needs and reflect their values. However, the process of identifying, reaching out, and engaging with all relevant stakeholder groups, which we refer to as recruitment methodology, is still a practical challenge in AI projects striving to adopt participatory practices. In this paper, we investigate the challenges that researchers face when designing and executing recruitment methodology for Participatory AI projects, and the implications of current recruitment practice for Participatory AI. First, we describe the recruitment methodologies used in AI projects using a corpus of 37 projects to capture the diversity of practices in the field and perform an initial analysis on the documentation of recruitment practices, as well as specific strategies that researchers use to meet goals of equity and empowerment. To complement this analysis, we interview five AI researchers to learn about the outcomes of recruitment methodologies. We find that these outcomes are shaped by structural conditions of their work, researchers' own goals and expectations, and the relationships built from the recruitment methodology and subsequent collaboration. Based on these analyses, we provide recommendations for designing and executing relationship-forward recruitment methods, as well as reflexive recruitment documentation practices for Participatory AI researchers.