Multiple Disciplinary Data Work Practices in Artificial Intelligence Research: a Healthcare Case Study in the UK

Developing artificial intelligence (AI) tools for healthcare is a multiple disciplinary effort, bringing data scientists, clinicians, patients and other disciplines together. In this paper, we explore the AI development workflow and how participants navigate the challenges and tensions of sharing and generating knowledge across disciplines. Through an inductive thematic analysis of 13 semi-structured interviews with participants in a large research consortia, our findings suggest that multiple disciplinarity heavily impacts work practices. Participants faced challenges to learn the languages of other disciplines and needed to adapt the tools used for sharing and communicating with their audience, particularly those from a clinical or patient perspective. Large health datasets also posed certain restrictions on work practices. We identified meetings as a key platform for facilitating exchanges between disciplines and allowing for the blending and creation of knowledge. Finally, we discuss design implications for data science and collaborative tools, and recommendations for future research.