  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.
