The Chinese social credit system (SCS) is a digital sociotechnical credit system that rewards and sanctions the economic and social behaviors of individuals and companies. This article uses classic social-control theories—the shaming theory and the labeling theory—to analyze the SCS, thereby contributing to a better understanding of the Chinese social-control approach to the digital transformation of society. Our research relies not only on government documents and media reports, but also on first-hand data collected from in-depth interviews conducted in China. We found that the perceived effectiveness of the shaming and labeling mechanisms is enhanced by the design of the SCS framework and the assistance of digital technology but weakened by a lack of transparency and questionable justification criteria, as well as privacy and fairness concerns.

The Chinese Social Credit System (SCS), the first nationwide digitally implemented social rating system, aims to enhance trustworthiness within Chinese society and serves as a critical example of digital transformation of society. It is further designed to improve moral behavior, financial reliability, and social control. The implementation of the SCS is based on a large scale of personal information collection, processing, evaluation, and disclosure, which raises serious privacy concerns. This chapter briefly introduces the SCS before delving into the privacy issues raised by the SCS's two branches: the government-run SCS and the commercial branch. We discuss three major privacy concerns. First, personal information disclosed via the SCS blacklists and redlists is a considerable challenge to privacy and even security. Second, the SCS framework further facilitates personal data flows from the private sector to the government. Third, although the Chinese legal environment for privacy protection is evolving, enforcement is lacking. Finally, we shed light on using SCS data for research.

On both global and local levels, one can observe a trend toward the adoption of algorithmic regulation in the public sector, with the Chinese social credit system (SCS) serving as a prominent and controversial example of this phenomenon. Within the SCS framework, cities play a pivotal role in its development and implementation, both as evaluators of individuals and enterprises and as subjects of evaluation themselves. This study engages in a comparative analysis of SCS scoring mechanisms for individuals and enterprises across diverse Chinese cities while also scrutinizing the scoring system applied to cities themselves. We investigate the extent of algorithmic regulation exercised through the SCS, elucidating its operational dynamics at the city level in China and assessing its interventionism, especially concerning the involvement of algorithms. Furthermore, we discuss ethical concerns surrounding the SCS’s implementation, particularly regarding transparency and fairness. By addressing these issues, this article contributes to two research domains: algorithmic regulation and discourse surrounding the SCS, offering valuable insights into the ongoing utilization of algorithmic regulation to tackle governance and societal challenges.

In recent years, credit reporting agencies are under pressure to reform themselves to reach higher levels of transparency. In this context, the critically considered digitally-implemented credit reporting system in China—the Chinese Social Credit System (SCS)—is of particular interest. Compared to traditional credit reporting systems, the SCS has a wider range of goals, including enhancing social control, establishing a state-defined notion of trustworthiness and increasing market efficiency. It covers not only Chinese citizens, companies, and organizations but also foreign ones which have activities in or with China. This paper studies the transparency of the SCS from the perspective of foreign organizations. We conducted interviews with employees from German organizations that are engaged in commercial activities in or with China to understand how transparent the SCS is to them and to gather their perceptions about the role of the system in increasing the transparency of the Chinese business landscape. Our analysis of the interviews showcased an interesting contrast between reservations with respect to the system’s transparency and the belief that the SCS would enhance the transparency of the Chinese business landscape. Drawing on our analysis of the interviews, we discuss factors affecting SCS transparency and the role of the SCS in increasing corporate transparency. We further highlight that there are inherent limits when credit rating systems (such as the SCS) aim to provide transparency about market participants, while pursuing other objectives at the same time.

Recent developments in artificial intelligence research have advanced the spread of automated decision-making (ADM) systems used for regulating human behaviors. In this context, prior work has focused on the determinants of human trust in and the legitimacy of ADM systems, e.g., when used for decision support. However, studies assessing people's perceptions of ADM systems used for behavioral regulation, as well as the effect on behaviors and the overall impact on human communities are largely absent. In this paper, we experimentally investigate people's behavioral adaptations to, and their perceptions of an institutionalized decision-making system, which resembled a social scoring system. Using social scores as incentives, the system aimed at ensuring mutual fair treatment between members of experimental communities. We explore how the provision of transparency affected people’s perceptions, behaviors, as well as the well-being of the communities. While a non-transparent scoring system led to disparate impacts both within as well as across communities, transparency helped people develop trust in each other, create wealth, and enabled them to benefit from the system in a more uniform manner. A transparent system was perceived as more effective, procedurally just, and legitimate, and led people to rely more strongly on the system. However, transparency also made people strongly discipline those with a low score. This suggests that social scoring systems that precisely disclose past behaviors may also impose significant discriminatory consequences on individuals deemed non-compliant.

As the first nationwide reputation system, the Chinese Social Credit System (SCS) employs a plethora of information technologies to rate citizens according to a state-defined notion of trustworthiness. Complementing extant research about the SCS that highlights important concerns regarding privacy and surveillance, our work explores the economic facets of the system on the basis of microeconomic and informational theories. We identify reputational discrimination as the basic working principle of the SCS, and propose that in addition to carrots and sticks, which are often highlighted as the main tools of the SCS to incentivize individuals to trustworthy behavior, status is another important variable the planner disposes of. Using a contest model approach, we find that when status is used as a complementary instrument to rewards and punishments, a planner who cares about efficiency, morality, and strictness treats most unfair those that are already bad off. Our work adds another dimension to the policy analysis of the SCS, and advances the understanding of the informational design of the SCS by investigating the fairness-related concerns that the system raises for individuals. Our findings underscore the importance of incorporating fairness principles into the SCS, to not further widen existing economic and societal gaps.