Abstract
The goal of this paper is to outline methodological insights and tips that organizational ethnographers can employ in studying the development of machine learning models (“ML tools”) to better understand the resulting ML tool and its organizational consequences. While proliferating at a swift pace across different organizations, ML tools do not easily lend themselves to be observed because they are dynamic and frequently changing. Recognizing this challenge, we propose focusing on data work as a way to capture the concrete traces that help make sense of the resulting ML tool’s intelligent functions which are often described as inscrutable. We draw on illustrative examples from our fieldwork experiences in two teaching hospitals in the Netherlands and China, where we observed the data work involved in creating ML tools. Along the three stages of data work performed during ML tool development: collecting, annotating, and recalibrating, we propose methodological insights and tips that can help ethnographers uncover how ML tools are shaped by relations among entities, both pre‐existing and emerging, across levels of analysis.