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.