How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

TL;DR

This paper examines how AI developers address communication challenges in multidisciplinary teams, focusing on knowledge mismatches, communication strategies, and trust management through a case study approach.

Contribution

It provides insights into the communication gaps faced by AI developers and how they bridge disciplinary and organizational boundaries in collaborative AI projects.

Findings

Identification of key communication gaps in multidisciplinary AI teams

Strategies used by AI developers to communicate complex data science concepts

Insights into trust and expectation management in cross-disciplinary collaboration

Abstract

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.