Description
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.
Table of Contents
About the EditorsContributorsPart I: Introduction: The Past, Present, and Future of Machine Learning in Sociology1. Sociology and Machine Learning Juan Pablo Pardo-Guerra and Christian Borch2. Machine Learning in Sociology: Current and Future ApplicationsFiliz Garip and Michael W. Macy3. How Machine Learning Became PervasiveEmilio LehoucqPart II: Machine Learning as a Methodological Toolbox4. Corpus Modeling and the Geometries of Text: Meaning Spaces as Metaphor and MethodDustin S. Stoltz, Marissa A. Combs, and Marshall A. Taylor5. Sociolinguistic Perspectives on Machine Learning with Textual DataAJ Alvero6. Chinese Computational Sociology: Decolonial Applications of Machine Learning and Natural Language Processing Methods in Chinese-Language ContextsLinda Hong Cheng and Yao Lu7. Hate Speech Detection and Bias in Supervised Text ClassificationThomas R. Davidson8. Analyzing Image Data with Machine LearningHan Zhang9. Sociogeographical Machine Learning: Using Machine Learning to Understand the Social Mechanisms of PlaceRolf Lyneborg Lund10. The Machine Learning of Sound and Music in Sociological ResearchKe Nie11. Munging the Ghosts in the Machine: Coded Bias and the Craft of Wrangling Archival DataVincent Yung and Jeannette A. Colyvas12. Fitting Paradox: Machine Learning Algorithms vs Statistical ModelingEun Kyong Shin13. Predictability Hypotheses: A Meta-Theoretical and Methodological IntroductionAustin van Loon14. Ethnography and Machine Learning: Synergies and New DirectionsZhuofan Li and Corey M. Abramson15. Machine Learning, Abduction, and Computational EthnographyPhilipp BrandtPart III: Societal Machine Learning Applications16. Machine Learning, Infrastructures, and their Sociomaterial PossibilitiesJuan Pablo Pardo-Guerra17. Race and Intersecting Inequalities in Machine LearningSharla Alegria18. Gender, Sex, and the Constraints of Machine Learning MethodsJeffrey W. Lockhart19. Facial Recognition in Law EnforcementJens Hälterlein20. Machine Learning in Chinese courtsNyu Wang and Michael Yuan Tian21. A Tale of Two Social Credit Systems: The Succeeded and Failed Adoption of Machine Learning in Sociotechnical InfrastructuresChuncheng Liu22. Machine Learning as a State Building Experiment: AI and Development in AfricaYousif Hassan23. The Use and Promises of Machine Learning in Financial Markets: From Mundane Practices to Complex Automated SystemsTaylor Spears and Kristian Bondo Hansen24. Machine Learning and Large-scale Data for Understanding Urban InequalityJennifer Candipan and Jonathan Tollefson25. Epistemic Infrastructures of Moral Decision-Making in the Ethics of Autonomous DrivingMaya Indira Ganesh26. Machine Learning in Medical Systems: Toward a Sociological AgendaWanheng Hu27. Machine Learning in the Arts and Cultural and Creative IndustriesMariya Dzhimova28. Environment, Society, and Machine LearningCaleb Scoville, Hilary Faxon, Melissa Chapman, Samantha Jo Fried, Lily Xu, Carl Boettiger, J. Michael Reed, Marcus Lapeyrolerie, Amy Van Scoyoc, Razvan Amironesei29. Coding and ExpertiseAlex PredaPart IV: Machine Learning and Sociological Theory30. How Machine Learning is Reviving Sociological TheorizationLaura K. Nelson and Jessica J. Santana31. Quality Control for Quality Computational Concepts: Wrangling with Theory and Data Wrangling as TheorizingVincent Yung, Jeannette A. Colyvas, and Hokyu Hwang32. Machine Agencies: Large Language Models as a Case for a Sociology of MachinesCeyda Yolgörmez33. Meaning and MachinesOscar Stuhler, Dustin S. Stoltz, and John Levi Martin34. Machine Learning and the Analysis of CultureSophie Mützel and Étienne Ollion35. Estimating Social Influence Using Machine Learning and Digital Trace DataMartin Arvidsson and Marc Keuschnigg36. Computational Authority in Platform Society: Dimensions of Power in Machine LearningMassimo Airoldi37. Predictive Analytics: A Sociological PerspectiveSimon Egbert38. Theoretical Challenges of Human-Machine Interaction Towards a Sociology of InterfacesBenjamin Lipp and Henning Mayer39. Colonialities of Machine LearningChristian Borch
