Situating machine learning – On the calibration of problems in practice

ABSTRACT

In this paper, we employ John Dewey’s notion of the situation as an analytic lens for observing and theorizing machine learning. Based on two ethnographic case studies in art and science, we account for machine learning as practice and examine the dynamics of the situations it gives rise to. Following Dewey, our observations focus on the transformation of situations from an initial state of indeterminacy through to problematizations and their resolution. Rethinking machine learning through the situation, we analyze how cooperating machine learners, both human and non-human, resolve situations and thereby refine their mutual attunement. With Dewey, we first explain how machine learners train through disruption and adaptation as they identify and solve problems. Second, we show that these problems concern issues of latency and addressability in efforts of cooperation between heterogeneous machine learners. Third, we discuss how machine learning practices cultivate situations that feature careful calibrations of problems that allow for their productive transformation. Our empirically grounded approach offers a pragmatist account of machine learning as a continually indeterminate and dynamic situated practice. As a contribution to ongoing discussions in social theory, we reframe existing characterizations of machine learning as issues of latency and addressability in cooperation.

KEYWORDS:

• Artificial intelligence
• machine learning
• technology
• situation
• contingency
• indeterminacy
• pragmatism
• ethnography

Acknowledgements

We thank Celia Brightwell for her many helpful remarks and subtle stylistic advice on the manuscript. We also wish to thank the anonymous referees and the editors of this issue whose comments and suggestions helped improve this paper considerably.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The research was funded by The Schaufler Foundation and TU Dresden.

Notes on contributors

Richard Groß

Richard Groß is a PhD candidate in sociology and a fellow at Schaufler Lab@TU Dresden, Germany. He has researched digital technologies, systems theory, philosophical anthropology, and the sociology of time.

Susann Wagenknecht

Susann Wagenknecht holds the Junior Professorship in Micro-Sociology and Techno-Social Interaction at the Institute of Sociology at Technische Universität Dresden, Germany. She obtained her dissertation (title: A Social Epistemology of Research Groups) from Aarhus University, Denmark. Drawing on ethnography, sociology, and STS, Susann researches the design, maintenance, and use of technologies and infrastructures.