The Oxford Handbook of the Sociology of Machine Learning

This volume is the first comprehensive collection of essays exploring machine learning from a sociological perspective. It offers a thorough introduction to various forms of machine learning and features articles on three main themes: (a) how machine learning can be used as a methodological tool in sociological research; (b) what sociology can reveal about the biases and societal impacts of the growing use of machine learning; and (c) how machine learning affects the way sociological theories are developed. This collection is designed for academics and students in sociology, as well as those interested in the broader social sciences.

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.