  Collaborative human-AI (HAI) teaming combines the unique skills and
capabilities of humans and machines in sustained teaming interactions
leveraging the strengths of each. In tasks involving regular exposure to
novelty and uncertainty, collaboration between adaptive, creative humans and
powerful, precise artificial intelligence (AI) promises new solutions and
efficiencies. User trust is essential to creating and maintaining these
collaborative relationships. Established models of trust in traditional forms
of AI typically recognize the contribution of three primary categories of trust
antecedents: characteristics of the human user, characteristics of the
technology, and environmental factors. The emergence of HAI teams, however,
requires an understanding of human trust that accounts for the specificity of
task contexts and goals, integrates processes of interaction, and captures how
trust evolves in a teaming environment over time. Drawing on both the
psychological and computer science literature, the process framework of trust
in collaborative HAI teams (CHAI-T) presented in this paper adopts the
tripartite structure of antecedents established by earlier models, while
incorporating team processes and performance phases to capture the dynamism
inherent to trust in teaming contexts. These features enable active management
of trust in collaborative AI systems, with practical implications for the
design and deployment of collaborative HAI teams.
