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This position paper offers a framework to think about how to better involvehuman influence in algorithmic decision-making of contentious public policyissues. Drawing from insights in communication literature, we introduce a"public(s)-in-the-loop" approach and enumerates three features that are centralto this approach: publics as plural political entities, collectivedecision-making through deliberation, and the construction of publics. Itexplores how these features might advance our understanding of stakeholderparticipation in AI design in contentious public policy domains such asrecidivism prediction. Finally, it sketches out part of a research agenda forthe HCI community to support this work.

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