Power to the People? Opportunities and Challenges for Participatory AI

By Abeba Birhane, William S. Isaac, Vinodkumar Prabhakaran, Mark Díaz, Madeleine Clare Elish, Iason Gabriel, Shakir Mohamed. 2022

422 citations recorded by the source corpus.

Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum: the increased attention comes partly with the view that participation opens the gateway to an inclusive, equitable, robust, responsible and trustworthy AI. Among other benefits, participatory approaches are essential to understanding and adequately representing the needs, desires and perspectives of historically marginalized communities. However, there currently exists lack of clarity on what meaningful participation entails and what it is expected to do. In this paper we first review participatory approaches as situated in historical contexts as well as participatory methods and practices within the AI and ML pipeline. We then introduce three case studies in participatory AI. Participation holds the potential for beneficial, emancipatory and empowering technology design, development and deployment while also being at risk for concerns such as cooptation and conflation with other activities. We lay out these limitations and concerns and argue that as participatory AI/ML becomes in vogue, a contextual and nuanced understanding of the term as well as consideration of who the primary beneficiaries of participatory activities ought to be constitute crucial factors to realizing the benefits and opportunities that participation brings.

Subfields: Computer Science Applications, Safety Research, Safety, Robustness & Interpretability, Human-Computer Interaction

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Citations in the corpus

Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Participatory methodologies in AI development have [31] included engaging locally led and compensated focus groups with impacted community members, in collaboration with engineers, to think through potential harmful outcomes.”
Improving alignment of dialogue agents via targeted human judgements (2022, background): “Participatory approaches are not a panacea, however, and their successful deployment turns on a set of technical and ethical considerations that have been well documented in prior research on sociotechnical ML (Birhane et al., 2022; Sloane et al., 2020).”
BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model (2022, background): “The beneﬁts of such an inclusive and diverse participatory approach to research, what Birhane et al. (2022) call the “partic-ipatory turn” of AI research, goes well beyond the Big Science metaphor and is indeed well aligned with trends observed by ?”
Participatory provenance as representational auditing for AI-mediated public consultation (2026, unknown): “The participatory AI literature has highlighted power dynamics in system design [Sloane et al., 2022, Birhane et al., 2022, Delgado et al., 2023] and has called for meaningful involvement of affected communities [Young, 2000, Fung, 2006], but has not produced formal metrics for auditing live…”