Abstract: The collection, curation, and analysis of data has always been as social as it is technical. As the statistical techniques and computational infrastructures of data science rapidly develop, we must continue to ground our understandings of data in context, drawing on the lived experiences of people who give that data meaning. But how do we bring human-centered perspectives and cultural contexts to data science? In this talk, I define and discuss two ways of thinking about ethnographic methods in relation to computation and data science, then discuss how my research into various knowledge infrastructures and user-generated content platforms relates to both. First, the ethnography of computation involves using traditional ethnographic methods (e.g. interviews, observation, participant-observation, case studies, and archival research) to study how people relate to computation and data in various ways. How do people design, develop, deploy, document, debate, maintain, manage, use, not use, learn, or teach computation and data in their everyday life and work Second, computational ethnography involves extending ethnography's traditionally qualitative methodological toolkit with computational methods. How can we conduct mixed-method scholarship in line with the broader epistemological principles that make ethnography a rich method for holistically investigating cultural phenomena? Both approaches bring key insights and collaborations to many classic and contemporary issues about information systems as socio-technical systems, letting us attend to data, information, and knowledge as it exists in particular organizational, institutional, social, cultural, economic, and political contexts.
Stuart Geiger is a staff ethnographer and postdoctoral scholar at the UC-Berkeley Institute for Data Science, where he studies the infrastructures and institutions that support the production of knowledge. His PhD research at the UC-Berkeley School of Information investigated the governance and moderation of Wikipedia and Twitter, focusing on the social and organizational roles of algorithmic systems. He is a methodological and disciplinary pluralist who builds bridges between ways of knowing, participating in the fields of social informatics, Computer-Supported Cooperative Work, Science & Technology Studies, communication and media studies, organizational sociology, machine learning, and open source software development. Stuart is also a founding member of UC-Berkeley's cross-departmental working groups on Data Science Studies, Algorithms in Culture, and Algorithmic Opacity & Fairness.
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