The Oxford Handbook of the Sociology of Machine Learning

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.

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.

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