Blacklists and Redlists in the Chinese Social Credit System: Diversity, Flexibility, and Comprehensiveness

Abstract

The Chinese Social Credit System (SCS) is a novel digital socio-technical credit system. The SCS aims to regulate societal behavior by reputational and material devices. Scholarship on the SCS has offered a variety of legal and theoretical perspectives. However, little is known about its actual implementation. Here, we provide the first comprehensive empirical study of digital blacklists (listing "bad" behavior) and redlists (listing "good" behavior) in the Chinese SCS. Based on a unique data set of reputational blacklists and redlists in 30 Chinese provincial-level administrative divisions (ADs), we show the diversity, flexibility, and comprehensiveness of the SCS listing infrastructure. First, our results demonstrate that the Chinese SCS unfolds in a highly diversified manner: we find differences in accessibility, interface design and credit information across provincial-level SCS blacklists and redlists. Second, SCS listings are flexible. During the COVID-19 outbreak, we observe a swift addition of blacklists and redlists that helps strengthen the compliance with coronavirus-related norms and regulations. Third, the SCS listing infrastructure is comprehensive. Overall, we identify 273 blacklists and 154 redlists across provincial-level ADs. Our blacklist and redlist taxonomy highlights that the SCS listing infrastructure prioritizes law enforcement and industry regulations. We also identify redlists that reward political and moral behavior. Our study substantiates the enormous scale and diversity of the Chinese SCS and puts the debate on its reach and societal impact on firmer ground. Finally, we initiate a discussion on the ethical dimensions of data-driven research on the SCS.