How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
It addresses practical collaboration issues for AI developers working with non-technical stakeholders, but is incremental as it builds on existing shared mental models theory.
The paper investigates communication challenges faced by AI developers in multidisciplinary teams, identifying types of gaps and strategies used to bridge them through interviews and artifact analysis.
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.