The Cultural Life of Machine Learning

An Incursion into Critical AI Studies

Editors: Roberge, Jonathan, Castelle, Michael (Eds.)

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  • Offers an empirically-based discussion of machine learning and artificial intelligence technologies in society
  • Provides new histories of machine learning and artificial intelligence rooted in humanities and social sciences approaches
  • Places technologies in conversation with central theories and topics in the humanities and social sciences
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About this book

This book brings together the work of historians and sociologists with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMind’s AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents’ capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of “learning” does machine learning truly represent? We empirically address these questions and more to provide a baseline for future research.
Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

About the authors

Jonathan Roberge is an Associate Professor at the Institut National de la Recherche Scientifique in Montreal, Canada. He funded the Nenic Lab as part of the Canada Research Chair in Digital Culture he has held since 2012. His most recent edited volume is Algorithmic Cultures (2016).

Michael Castelle is an Assistant Professor at the University of Warwick’s Centre for Interdisciplinary Methodologies, UK and a Turing Fellow at the Alan Turing Institute, UK. He has a Ph.D. in Sociology from the University of Chicago and a Sc.B. in Computer Science from Brown University.




Table of contents (9 chapters)

Table of contents (9 chapters)
  • Toward an End-to-End Sociology of 21st-Century Machine Learning

    Pages 1-29

    Roberge, Jonathan (et al.)

  • Mechanized Significance and Machine Learning: Why It Became Thinkable and Preferable to Teach Machines to Judge the World

    Pages 31-78

    Mendon-Plasek, Aaron

  • What Kind of Learning Is Machine Learning?

    Pages 79-115

    Reigeluth, Tyler (et al.)

  • The

    Pages 117-142

    McKelvey, Fenwick

  • Machinic Encounters: A Relational Approach to the Sociology of AI

    Pages 143-166

    Yolgörmez, Ceyda

Buy this book

eBook $84.99
price for USA
  • ISBN 978-3-030-56286-1
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $109.99
price for USA
  • ISBN 978-3-030-56285-4
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock

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Bibliographic Information

Bibliographic Information
Book Title
The Cultural Life of Machine Learning
Book Subtitle
An Incursion into Critical AI Studies
Editors
  • Jonathan Roberge
  • Michael Castelle
Copyright
2021
Publisher
Palgrave Macmillan
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-56286-1
DOI
10.1007/978-3-030-56286-1
Hardcover ISBN
978-3-030-56285-4
Edition Number
1
Number of Pages
XV, 289
Number of Illustrations
3 b/w illustrations, 7 illustrations in colour
Topics