Archaeology of a Data Practice
272 pp., 7 x 9 in, 25 b&w illus., 15 tables
- Published: December 8, 2017
- Publisher: The MIT Press
- Published: November 16, 2017
- Publisher: The MIT Press
If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought?
Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking.
Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures.
Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.
Adrian Mackenzie's insightful book details the many technical aspects of machine learning while also bringing it into conversation with cultural theory and science and technology studies.
Jussi Parikka, Professor, Technological Culture & Aesthetics, and Director, Archaeologies of Media and Technology, Winchester School of Art, University of Southampton
This book breaks remarkable ground in offering a situated and deeply empirical account of contemporary analytic practices using big data. Mackenzie produces a novel and nuanced analysis of how population, knowledge, and power are being transformed through statistical modes of machine learning. Heavily researched, compelling in its arguments, and unique for interrogating the power relations inherent within machine learning, Mackenzie provides not only a path to understanding the new relationships between big data and machine learning that are transforming our contemporary world, but also a guidebook to tactics, methods, and practices that might allow concerned practitioners in many fields from the humanities to the computational sciences to rethink naturalized practices and to reimagine what both learning and data might become.
Orit Halpern, Associate Professor, Department of Sociology and Anthropology, Concordia University