A Tale of Two Social Credit Systems: The Succeeded and Failed Adoption of Machine Learning in Sociotechnical Infrastructures
Oxford Handbook of the Sociology of Machine Learning, edited by Christian Borch and Juan Pablo Pardo-Guerra. Oxford: University of Oxford Press, Forthcoming
26 Pages Posted: 8 Jun 2023 Last revised: 25 Oct 2023
Date Written: October 21, 2023
Abstract
Machine learning technologies have permeated diverse sectors, catalyzing transformative shifts in the understanding, management, and navigation of complex sociotechnical systems. However, how are machine learning technologies adopted in different scenarios, and what are the necessary sociotechnical conditions? This chapter undertakes a comparative analysis of machine learning technologies adoption in two Chinese social credit systems. The central argument of this chapter revolves around two primary components: diverse data input and well-defined outcomes. Both elements are fundamental to the effective deployment of machine learning models and influence their accuracy, relevance, and utility. The success or failure of machine learning adoption is not solely a technical or social matter. Instead, as the chapter underscores, there is an intricate balance between technical prowess and social compatibility, both of which are indispensable for successful technology adoption.
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