The cultural life of machine learning : an incursion into critical AI studies / Jonathan Roberge, Michael Castelle, editors.
2021
Q325.5
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Title
The cultural life of machine learning : an incursion into critical AI studies / Jonathan Roberge, Michael Castelle, editors.
ISBN
9783030562861 (PDF ebook)
3030562867 (PDF ebook)
9783030562854 (hbk.)
3030562859
3030562867 (PDF ebook)
9783030562854 (hbk.)
3030562859
Published
Cham, Switzerland : Palgrave Macmillan, [2021]
Language
English
Description
1 online resource : illustrations (black and white, and colour)
Item Number
10.1007/978-3-030-56286-1 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
303.4834
303.4834
Summary
This book brings together the work of sociologists and historians along 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 DeepMinds 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? Contributors empirically address these questions and more to provide a baseline for future research. 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 Warwicks 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. Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com
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Includes bibliographical references and index.
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Online resource; title from PDF title page (SpringerLink, viewed February 24, 2021).
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Print version : 9783030562854
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Table of Contents
1. Toward an End-to-End Sociology of 21st-Century Machine Learning
2. Mechanized Significance and Machine Learning: Why it Became Thinkable and Preferable to Teach Machines to Judge the World
3. What Kind of Learning Is Machine Learning?
4. The Other Cambridge Analytics: Early "Artificial Intelligence" in American Political Science
5. Machinic Encounters: A Relational Approach to the Sociology of AI
6. AlphaGos Deep Play: Technological Breakthrough as Social Drama
7. Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge
8. Planetary Intelligence
9. Critical Perspectives on Governance Mechanisms for AI/ML Systems.
2. Mechanized Significance and Machine Learning: Why it Became Thinkable and Preferable to Teach Machines to Judge the World
3. What Kind of Learning Is Machine Learning?
4. The Other Cambridge Analytics: Early "Artificial Intelligence" in American Political Science
5. Machinic Encounters: A Relational Approach to the Sociology of AI
6. AlphaGos Deep Play: Technological Breakthrough as Social Drama
7. Adversariality in Machine Learning Systems: On Neural Networks and the Limits of Knowledge
8. Planetary Intelligence
9. Critical Perspectives on Governance Mechanisms for AI/ML Systems.