Juan Pablo Pardo-Guerra

The Oxford Handbook of the Sociology of Machine Learning (eBook, ePUB)

Redaktion: Borch, Christian

• Format: ePub

Produktbeschreibung

Machine learning, renowned for its ability to detect patterns in large datasets, has seen a significant increase in applications and complexity since the early 2000s. The Oxford Handbook of the Sociology of Machine Learning offers a state-of-the-art and forward-looking overview of the intersection between machine learning and sociology, exploring what sociology can gain from machine learning and how it can shed new light on the societal implications of this technology. Through its 39 chapters, an international group of sociologists address three key questions. First, what can sociologists yield from using machine learning as a methodological tool? This question is examined across various data types, including text, images, and sound, with insights into how machine learning and ethnography can be combined. Second, how is machine learning being used throughout society, and what are its consequences? The Handbook explores this question by examining the assumptions and infrastructures behind machine learning applications, as well as the biases they might perpetuate. Themes include art, cities, expertise, financial markets, gender, race, intersectionality, law enforcement, medicine, and the environment, covering contexts across the Global South and Global North. Third, what does machine learning mean for sociological theory and theorizing? Chapters examine this question through discussions on agency, culture, human-machine interaction, influence, meaning, power dynamics, prediction, and postcolonial perspectives. The Oxford Handbook of the Sociology of Machine Learning is an essential resource for academics and students interested in artificial intelligence, computational social science, and the role and implications of machine learning in society.

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Produktdetails

• Verlag: OUP eBook
• Seitenzahl: 808
• Erscheinungstermin: 25. März 2025
• Englisch
• ISBN-13: 9780197653623
• Artikelnr.: 75202703

Autorenporträt

Christian Borch is a professor of sociology at the University of Copenhagen. His current research focuses on automated trading in financial markets, exploring how machine learning is transforming market dynamics and leading to a reevaluation of sociological categories used to understand financial markets. His earlier historically focused work examined the development of sociological crowd theory and shifts in crime perceptions, both from the late nineteenth to the early twenty-first century. Before joining the University of Copenhagen, Borch was a Professor of Economic Sociology and Social Theory at the Copenhagen Business School. Juan Pablo Pardo-Guerra is a professor in sociology at the University of California, San Diego, a founding faculty member of the Halicio?lu Data Science Institute, co-founder of the Computational Social Science program, and Director of the Latin American Studies Program at UC San Diego. Prior to joining UC San Diego, Pardo-Guerra was an Assistant Professor at the London School of Economics and Political Science.

Inhaltsangabe

* About the Editors
* Contributors
* Part I: Introduction: The Past, Present, and Future of Machine
Learning in Sociology
* 1. Sociology and Machine Learning
* Juan Pablo Pardo-Guerra and Christian Borch
* 2. Machine Learning in Sociology: Current and Future Applications
* Filiz Garip and Michael W. Macy
* 3. How Machine Learning Became Pervasive
* Emilio Lehoucq
* Part II: Machine Learning as a Methodological Toolbox
* 4. Corpus Modeling and the Geometries of Text: Meaning Spaces as
Metaphor and Method
* Dustin S. Stoltz, Marissa A. Combs, and Marshall A. Taylor
* 5. Sociolinguistic Perspectives on Machine Learning with Textual Data
* AJ Alvero
* 6. Chinese Computational Sociology: Decolonial Applications of
Machine Learning and Natural Language Processing Methods in
Chinese-Language Contexts
* Linda Hong Cheng and Yao Lu
* 7. Hate Speech Detection and Bias in Supervised Text Classification
* Thomas R. Davidson
* 8. Analyzing Image Data with Machine Learning
* Han Zhang
* 9. Sociogeographical Machine Learning: Using Machine Learning to
Understand the Social Mechanisms of Place
* Rolf Lyneborg Lund
* 10. The Machine Learning of Sound and Music in Sociological Research
* Ke Nie
* 11. Munging the Ghosts in the Machine: Coded Bias and the Craft of
Wrangling Archival Data
* Vincent Yung and Jeannette A. Colyvas
* 12. Fitting Paradox: Machine Learning Algorithms vs Statistical
Modeling
* Eun Kyong Shin
* 13. Predictability Hypotheses: A Meta-Theoretical and Methodological
Introduction
* Austin van Loon
* 14. Ethnography and Machine Learning: Synergies and New Directions
* Zhuofan Li and Corey M. Abramson
* 15. Machine Learning, Abduction, and Computational Ethnography
* Philipp Brandt
* Part III: Societal Machine Learning Applications
* 16. Machine Learning, Infrastructures, and their Sociomaterial
Possibilities
* Juan Pablo Pardo-Guerra
* 17. Race and Intersecting Inequalities in Machine Learning
* Sharla Alegria
* 18. Gender, Sex, and the Constraints of Machine Learning Methods
* Jeffrey W. Lockhart
* 19. Facial Recognition in Law Enforcement
* Jens Hälterlein
* 20. Machine Learning in Chinese courts
* Nyu Wang and Michael Yuan Tian
* 21. A Tale of Two Social Credit Systems: The Succeeded and Failed
Adoption of Machine Learning in Sociotechnical Infrastructures
* Chuncheng Liu
* 22. Machine Learning as a State Building Experiment: AI and
Development in Africa
* Yousif Hassan
* 23. The Use and Promises of Machine Learning in Financial
Markets: From Mundane Practices to Complex Automated Systems
* Taylor Spears and Kristian Bondo Hansen
* 24. Machine Learning and Large-scale Data for Understanding Urban
Inequality
* Jennifer Candipan and Jonathan Tollefson
* 25. Epistemic Infrastructures of Moral Decision-Making in the Ethics
of Autonomous Driving
* Maya Indira Ganesh
* 26. Machine Learning in Medical Systems: Toward a Sociological Agenda
* Wanheng Hu
* 27. Machine Learning in the Arts and Cultural and Creative Industries
* Mariya Dzhimova
* 28. Environment, Society, and Machine Learning
* Caleb Scoville, Hilary Faxon, Melissa Chapman, Samantha Jo Fried,
Lily Xu, Carl Boettiger, J. Michael Reed, Marcus Lapeyrolerie, Amy
Van Scoyoc, Razvan Amironesei
* 29. Coding and Expertise
* Alex Preda
* Part IV: Machine Learning and Sociological Theory
* 30. How Machine Learning is Reviving Sociological Theorization
* Laura K. Nelson and Jessica J. Santana
* 31. Quality Control for Quality Computational Concepts: Wrangling
with Theory and Data Wrangling as Theorizing
* Vincent Yung, Jeannette A. Colyvas, and Hokyu Hwang
* 32. Machine Agencies: Large Language Models as a Case for a Sociology
of Machines
* Ceyda Yolgörmez
* 33. Meaning and Machines
* Oscar Stuhler, Dustin S. Stoltz, and John Levi Martin
* 34. Machine Learning and the Analysis of Culture
* Sophie Mützel and Étienne Ollion
* 35. Estimating Social Influence Using Machine Learning and Digital
Trace Data
* Martin Arvidsson and Marc Keuschnigg
* 36. Computational Authority in Platform Society: Dimensions of Power
in Machine Learning
* Massimo Airoldi
* 37. Predictive Analytics: A Sociological Perspective
* Simon Egbert
* 38. Theoretical Challenges of Human-Machine Interaction Towards a
Sociology of Interfaces
* Benjamin Lipp and Henning Mayer
* 39. Colonialities of Machine Learning
* Christian Borch