The Algorithm of Trust: The Sociotechnical Life of a Chinese Social Credit System
From China’s durable authoritarian governance to the recent COVID-19 pandemic crisis, we have observed the growth of the state power and capacity in seeing and governing citizens and society. Being boosted by the advancement of surveillance information and communication technologies (ICTs), hopes and concerns of data-driven governance have been increasingly raised. The Chinese social credit system, which is commonly perceived as the Chinese state’s attempt to expand the discipline of citizens’ everyday life by surveillance, datafication, and rating, epitomize these debates.
This study examines the cause, everyday practice, and consequences of the state’s datafication project. With the original ethnographic, interview, and archive data of one of the most detailed and well-implemented municipal SCS in a northern Chinese city, αCity – αSCS, I show that the data-driven never meet the promises it made and the expectations people had. I follow how the credit data is defined, collected, translated, represented, and interpreted, showing a different, yet more realistic picture of data-driven governance. I show the governance is neither objective as techno-utopianists claimed, nor omnipotent as techno-dystopianists imagined. Elaborating on the αSCS’s “failures” at different stages from the perspective of different actors and organizations, I reject two popular views of data-driven governance that have opposite stands yet with similar techno-determinist beliefs.