"Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic Decisions in Contentious Public Policy Domains

TL;DR

This paper proposes a 'public(s)-in-the-loop' framework to enhance human involvement and deliberation in algorithmic decision-making for contentious public policy issues, emphasizing stakeholder participation and collective decision-making.

Contribution

It introduces a novel framework integrating communication insights to involve publics in AI decision processes within contentious policy domains.

Findings

Highlights the importance of publics as political entities

Proposes collective deliberation for decision-making

Outlines a research agenda for HCI community involvement

Abstract

This position paper offers a framework to think about how to better involve human influence in algorithmic decision-making of contentious public policy issues. Drawing from insights in communication literature, we introduce a "public(s)-in-the-loop" approach and enumerates three features that are central to this approach: publics as plural political entities, collective decision-making through deliberation, and the construction of publics. It explores how these features might advance our understanding of stakeholder participation in AI design in contentious public policy domains such as recidivism prediction. Finally, it sketches out part of a research agenda for the HCI community to support this work.