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Palgrave Macmillan

The Cultural Life of Machine Learning

An Incursion into Critical AI Studies

  • Book
  • © 2021

Overview

Editors:
  1. Jonathan Roberge

    1. Centre Urbanisation Culture Société, Institut national de la recherche scientifique, Quebec City, Canada

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  2. Michael Castelle

    1. Centre for Interdisciplinary Methodologies, University of Warwick, Coventry, UK

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    You can also search for this editor in PubMed   Google Scholar

  • 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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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xv
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  2. Toward an End-to-End Sociology of 21st-Century Machine Learning

    • Jonathan Roberge, Michael Castelle
    Pages 1-29

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

    • Aaron Mendon-Plasek
    Pages 31-78Open Access

  4. What Kind of Learning Is Machine Learning?

    • Tyler Reigeluth, Michael Castelle
    Pages 79-115

  5. The Other Cambridge Analytics: Early “Artificial Intelligence” in American Political Science

    • Fenwick McKelvey
    Pages 117-142

  6. Machinic Encounters: A Relational Approach to the Sociology of AI

    • Ceyda Yolgörmez
    Pages 143-166

  7. AlphaGo’s Deep Play: Technological Breakthrough as Social Drama

    • Werner Binder
    Pages 167-195

  8. Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge

    • Théo Lepage-Richer
    Pages 197-225

  9. Planetary Intelligence

    • Orit Halpern
    Pages 227-256

  10. Critical Perspectives on Governance Mechanisms for AI/ML Systems

    • Luke Stark, Daniel Greene, Anna Lauren Hoffmann
    Pages 257-280

  11. Back Matter

    Pages 281-289
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Keywords

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.

Editors and Affiliations

  • Centre Urbanisation Culture Société, Institut national de la recherche scientifique, Quebec City, Canada

    Jonathan Roberge

  • Centre for Interdisciplinary Methodologies, University of Warwick, Coventry, UK

    Michael Castelle

About the editors

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.






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