Autoethnographic Methods for Studying Data-Driven Knowledge Production

This presentation is based on a collaborative, multisided ethnography of data science, in which we have been embedded in aligned institutes dedicated to data science. In this paper, I focus on autoethnographic methods, which can be powerful and generative ways to conduct empirical investigations into data science practices across many theoretical issues. This paper reviews several different exercises, initiatives, and activities that we have conducted in our fieldwork, reflecting on how they help us better understand different aspects of what it means to do data science.