"Crowdsourced Cats: Machine Learning as Culture in Chinese Governance"
Jamie Wong, PhD candidate in the History, Anthropology, Science, Technology, and Society (HASTS) program, Massachusetts Institute of Technology.
Co-sponsored by the Graduate School of Arts and Sciences and the School of Engineering and Applied Sciences, Harvard University.
Sheila Jasanoff, Faculty Associate. Pforzheimer Professor of Science and Technology Studies; Professor of Environmental Science and Public Policy, Committee on Degrees in Environmental Science and Public Policy, Harvard Kennedy School.
Drawing on ethnographic research on China’s “business-to-government” (B2G) market, this paper highlights how local governments and the start-up companies they work with conceive of themselves as policy-generating nodes within a nationwide machine learning assemblage. Unpacking the logic of their analogy, I demonstrate how, like algorithms that learn from large data sets without being programmed explicitly, the Chinese state governs without specifying policy solutions for their national mandates. My interlocutors construe this computational analogy as simultaneously a reinterpretation and a seamless continuation of late paramount leader Deng Xiaoping's adage – "It doesn’t matter whether a cat is black or white; as long as it catches mice, it is a good cat” (不管黑猫白猫，捉到老鼠就是好猫). I discuss how this machine learning model of Chinese governance helps sustain a system of what I call “rule by correction.” Amidst evermore erratic local government policies and frequent central government crackdowns, I argue that contemporary Chinese citizens often perform “collectivism” by living with and enduring the turmoil of state intervention.