  AI and humans bring complementary skills to group deliberations. Modeling
this group decision making is especially challenging when the deliberations
include an element of risk and an exploration-exploitation process of
appraising the capabilities of the human and AI agents. To investigate this
question, we presented a sequence of intellective issues to a set of human
groups aided by imperfect AI agents. A group's goal was to appraise the
relative expertise of the group's members and its available AI agents, evaluate
the risks associated with different actions, and maximize the overall reward by
reaching consensus. We propose and empirically validate models of human-AI team
decision making under such uncertain circumstances, and show the value of
socio-cognitive constructs of prospect theory, influence dynamics, and Bayesian
learning in predicting the behavior of human-AI groups.
