  The development of AI applications is a multidisciplinary effort, involving
multiple roles collaborating with the AI developers, an umbrella term we use to
include data scientists and other AI-adjacent roles on the same team. During
these collaborations, there is a knowledge mismatch between AI developers, who
are skilled in data science, and external stakeholders who are typically not.
This difference leads to communication gaps, and the onus falls on AI
developers to explain data science concepts to their collaborators. In this
paper, we report on a study including analyses of both interviews with AI
developers and artifacts they produced for communication. Using the analytic
lens of shared mental models, we report on the types of communication gaps that
AI developers face, how AI developers communicate across disciplinary and
organizational boundaries, and how they simultaneously manage issues regarding
trust and expectations.
