"Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic Decisions in Contentious Public Policy Domains
This addresses the challenge of stakeholder engagement in AI for public policy domains like recidivism prediction, but it is incremental as it builds on existing communication literature and offers a conceptual framework rather than new empirical results.
The paper tackles the problem of involving human influence in algorithmic decision-making for contentious public policy issues by proposing a 'public(s)-in-the-loop' framework, which emphasizes plural publics, deliberation, and public construction to advance stakeholder participation in AI design.
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.