The mountains are high and the emperor is far away: Credit scoring and the infrastructure of surveillance capitalism in China
Ruowen Xu, Yuval Millo, Crawford Spence
Contemporary Accounting Research · 2024 · Volume 41 · Issue 2
Abstract
Previous research on calculative intermediaries shows how these effectively challenge, distort, and disrupt accounting practices in ways that policy-makers might not anticipate. The promises of surveillance capitalism—with its attendant data architectures, datafication processes, and technological sophistication—are different, supposing more accurate ways of reading individuals and greater calculative certainty overall. Yet there is little empirical research to explore how surveillance capitalism manifests itself at the organizational level, either conceptually or operationally. As a result, it remains uncertain whether such specters of omniscience are as haunting in reality as they appear in theory. We explore these themes by way of an ethnographic study into credit scoring in China, showing how intermediary organizations developed a multiplicity of credit scoring models based on machine learning and big data that differed both from original expectations and from each other. These different “renditions” of credit scoring suggest that the data architectures of surveillance capitalism are just as much subject to challenge and adaptation by intermediary organizations as calculative practices, such as accounting, are in more analog environments.
Keywords
big data, calculative intermediaries, credit scoring, machine learning, rendition, surveillance capitalism, apprentissage automatique, capitalisme de surveillance, évaluation du risque de crédit, intermédiaires de calcul, mégadonnées, rendu
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