The Oxford Handbook of the Sociology of Machine Learning
Professor Christian Borch has co-edited The Oxford Handbook of the Sociology of Machine Learning and contributed two chapters to the volume. The book explores the intersection of machine learning and sociology, highlighting both methodological innovation and critical perspectives.
Machine learning, renowned for its ability to detect patterns in large datasets, has seen a significant rise in both application and complexity since the early decades of the 21st century. 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 gain from using machine learning as a methodological tool? This question is examined across various data types, including text, images, and sound, and offers insights into how machine learning can be combined with ethnography.
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 may perpetuate. Themes include art, cities, expertise, financial markets, gender, race, intersectionality, law enforcement, medicine, and the environment, covering contexts across the Global South and the Global North. Third, what does machine learning mean for sociological theory and theorizing? Chapters engage 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.