Abstract
What is meant by a cultural life of machine learning (ML), and how can it be studied? How can one keep up with the rapid pace of ML’s technical development, while simultaneously offering analyses that “generalize” for the next generation? We propose the necessity for an end-to-end sociology of contemporary ML/AI which trains itself on the entire cultural process of ML research, production, and deployment, from historical genesis to sociotechnical innovation to political impact and back again. Along with the other chapters in this volume, we discuss how ML historically developed its claim to capture meaning; how its present-day relevance is tied to an increasingly cascading agency; and why one must critically address ML’s limited forms of interpretation in order to ultimately alter its course.
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Roberge, J., Castelle, M. (2021). Toward an End-to-End Sociology of 21st-Century Machine Learning. In: Roberge, J., Castelle, M. (eds) The Cultural Life of Machine Learning. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-56286-1_1
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