RelAItionship Building: Analyzing Recruitment Strategies for Participatory AI

2025-08-27

AI Summary

In participatory AI, stakeholder recruitment continues to face challenges—including identification bias, access barriers, and insufficient inclusivity—that undermine equity and empowerment goals. This study systematically examines structural limitations in recruitment practices through a literature review of 37 AI projects and in-depth interviews with 5 researchers, analyzed via qualitative content analysis. We introduce a novel “relationship-first” recruitment framework that foregrounds the dynamic interplay among structural conditions, researcher intent, and collaborative relationships, and propose reflective recruitment documentation standards. The work clarifies how recruitment practices fundamentally shape participation quality and offers actionable, relationship-centered design principles and implementation guidelines. By centering relationality and reflexivity, this research advances a methodological foundation for more inclusive and empowering participatory AI.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Analyzing recruitment challenges in Participatory AI projects
• Investigating stakeholder engagement strategies for AI development
• Evaluating relationship-building methodologies in participatory practices

Innovation

• Analyzing recruitment methodologies for Participatory AI
• Investigating challenges in stakeholder engagement strategies
• Providing relationship-forward recruitment recommendations