Competing, Collaborating, Compounding? Interactivity Between Public and Private Providers in China’s Social Credit System

• In Event: Critical Data Studies: Ethics and Human Contexts of Data Science II

Abstract

Since 2014, the Chinese government and several of China’s biggest technology firms have experimented with the creation of a social credit system in which data on web browsing, online purchases, social media, friend networks and a myriad of other behavioral and personal data sources are used to assign ratings of how trustworthy citizens are. Through discourse analysis of social credit policy planning documents, pilot project case studies, marketing materials, and Chinese news media coverage, this paper applies frameworks from the STS user studies, performativity, and valuation literature to analyze the models of compliance that state-run and privately operated social credit providers separately propose. How are social credit users and ratings co-produced in each of these models, and what assumptions are made about consumption of these evaluations as social capital? What are the ramifications for non-users? Using two widely documented examples—the Shanghai government’s Honest Shanghai pilot program and Alibaba spinoff Ant Financial’s scoring product Sesame Credit —the paper deconstructs the divide between these public and private algorithmic scoring initiatives. Government data inputs (e.g., national debtor blacklists) into ostensibly private social credit services, along with the growing use of privately-issued scores to access preferential treatment in housing markets, healthcare, foreign visa procurement and other public settings, blur the boundaries between these two types of social credit. The paper concludes with an agenda for empirical studies of how the collapse of these boundaries may obscure the purported objectives of each type of evaluative system, potentially creating discriminatory outcomes in both.

Author

• Shazeda Ahmed, School of Information, University of California- Berkeley