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

TL;DR

The paper investigates how brokers enable participatory ML by interviewing 18 brokers across diverse contexts to map incentives, strategies, and challenges. It reveals tensions between messy, context-rich participation and the rigid, data-centric ML workflow, and argues for a more activist broker role that educates end users and mediates dissent from indirect stakeholders. The findings illuminate how brokers shape problem formulation, data infrastructure, and evaluation, and offer guidance for sustaining equitable, end-user–centered Participatory ML while scaling practices through mass customization and localization. These insights advance practical guidance for design teams, researchers, and policymakers aiming to realize the democratic potential of Participatory ML.

Abstract

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.