From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research

Abstract

The AI Ethics community faces an imperative to empower stakeholders and impacted community members so that they can scrutinize and influence the design, development, and use of AI systems in high-stakes domains. While a growing chorus of recent papers has kindled interest in so-called "participatory ML"methods, precisely what form participation ought to take and how to operationalize these ambitions are seldom addressed. Our survey of the relevant literature shows that in many papers, participation is reduced to highly structured, computational mechanisms designed to elicit mathematically tractable approximations of narrowly-defined moral values. Of papers that actually engage with real people, these engagements typically consist of one-time interactions with individuals that are often unrepresentative of the relevant stakeholders. Motivated by these clear limitations, we introduce a consolidated set of axes to evaluate and improve participatory approaches. We use these axes to analyze contemporary work in this space and outline future AI research directions that could meaningfully contribute to operationalizing the ideal of participation.