Chinese Social Credit Systems
I am a leading scholar of the Chinese Social Credit System Project. Through national surveys, discourse analysis, and ethnography, my work replaces the image of a unified Orwellian monolith with a reality of fragmented, competing systems — revealing both the power and the limits of state control in the digital age. My open-access research is organized around the questions below.
Is there one unified “Big Brother” system?
Many assume the SCS is a single, centralized database. My early research (2019) corrected this misconception by mapping the institutional landscape of Chinese credit governance. I show that the SCS is not a monolithic Orwellian tool but a fragmented collection of initiatives — financial credit scores, national blacklists, and local municipal experiments — each operating with different logics.
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2019
In 2014, the Chinese government proposed to build a social credit system (SCS) to better collect and evaluate citizens’ creditworthiness, and grant rewards and punishments based on one’s social credit. Since then, various SCS pilots have been enacted. While current media and scholars often perceive SCS as a single and unified system, this paper argues that there are in fact multiple SCSs in China. I identify four main types of SCS and articulate the relationships among them. Each SCS has different assumptions, operationalizations, and implementations. China's central bank, People's Bank of China and the macroeconomic management agency National Development and Reform Commission are the two most important actors in the design and implementation of the multiple SCSs. Yet their distinctive views about what a "credit" is and what an SCS should be produced great tensions on the SCS landscape. I also historize current SCSs and show that many elements and assumptions of SCSs can be traced back to a broader People’s Republic of China’s (PRC) political history. At last, I propose an alternative theoretical framework to understand Chinese SCSs as a symbolic system with performative power that is more than a simple repressive and direct political project.
How is a SCS designed and implemented on the ground?
My more recent work focuses on the sociotechnical realities of local implementation. Through a detailed case study of a model municipal SCS metric, I demonstrate how local policy design can moralize existing inequality, subjecting government employees to intense surveillance while structurally disadvantaging rural residents. Based on interviews with policymakers, I also examine why high-tech tools like machine learning often fail in local governance despite their success in private finance — primarily due to a lack of data variety and clear administrative goals.
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2025
To govern, states collect and evaluate information about citizens. This paper examines a social credit system (SCS) in China, a state initiative aimed at governing trust through the quantification of social behavior. Our analysis opens the ‘black box’ of an SCS metric, investigating how trust is translated into numbers and the implications of this translation. We reveal that the SCS is deeply relational and embedded in specific interests, biases, and logics of governance. The system has the potential to reinforce structural injustices and inequalities as it particularly disadvantages rural residents. Meanwhile, it subjects government employees to stricter surveillance, indicating its multifaceted objectives. Our finding uncovers the nuanced ways the system interacts with social stratification in Chinese society and the administrative structure inside the state. We problematize the individualistic, decontextualized, and behavioral assumptions undergirding the metric, and advocate for a critical reassessment of the sociopolitical dimensions of such quantitative governance infrastructures.
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2025
Machine learning technologies have permeated diverse sectors, catalyzing transformative shifts in the understanding, management, and navigation of complex sociotechnical systems. However, how are machine learning technologies adopted in different scenarios, and what are the necessary sociotechnical conditions? This chapter undertakes a comparative analysis of machine learning technologies adoption in two Chinese social credit systems. The central argument of this chapter revolves around two primary components: diverse data input and well-defined outcomes. Both elements are fundamental to the effective deployment of machine learning models and influence their accuracy, relevance, and utility. The success or failure of machine learning adoption is not solely a technical or social matter. Instead, as the chapter underscores, there is an intricate balance between technical prowess and social compatibility, both of which are indispensable for successful technology adoption.
How do citizens navigate and respond to the SCSP?
Using national survey data, I reveal that public support for the SCS is surprisingly high but internally complex; counterintuitively, Chinese Communist Party members and high-status individuals often show more skepticism toward state surveillance than the general public. With Alexander Trauth-Goik, I use mixed methods to examine how the state attempts to weaponize social networks via blacklists, and how citizens often “grey” the black-and-white labels the state assigns.
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2022
Pervasive surveillance in modern society has raised mounting debates, which are largely concentrated on the ethical dimension and lack sociological examination. Drawing on innovative national survey data, this study analyzes public opinion about social credit systems (SCSs), an emerging infrastructure that expands the depth and breadth of surveillance in China. I find a general high support for expanding surveillance and punishment yet key variations among different social groups. Counterintuitively, people with higher political capital do not wholly embrace the expanding surveillance and punishment. For example, Chinese Communist Party members are less likely to support state-centered SCSs compared to the general public. Higher political trust in the regime and socioeconomic status is consistently correlated with higher support, while different media consumption showed limited correlations. This study proposes an alternative theorization of surveillance and enriches our understanding of the heterogeneity and dynamic of the state and public in the authoritarian regime.
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2023
Punishment and discipline from the state often do not only rely on formal state apparatuses, but also the mobilization of the deviant’s own social connections, such as family members or friends for informal discipline to enhance the power of social control. In China, such social punishment has been historically commonplace and today is widely used in the Social Credit System (SCS) that rewards “trustworthy” and punishes “untrustworthy” behaviour. This paper examines how relational punishment operate as part of the most consequential aspect of the SCS – the nation-wide Blacklist system. Previous studies have largely ignored how being blacklisted impacts the quality of commercial and interpersonal relations on a micro scale. This study utilises a mixed-method research design based on 30 interviews and a national survey to fill this empirical gap by examining how the Chinese public make sense of the Blacklist system and act upon blacklisted people to understand its power and limits. We first trace the history of blacklisting as a governance tool. We then illustrate how the state’s symbolic campaign constructs blacklisted people as morally tainted and pressures their friends and family to ostracize them. However, this power has its limits. People commonly differentiate the practice and character of blacklisted people with contextual and relational information, constructing alternative meanings for individuals thus labelled, therefore resisting the state’s symbolic enforcement, and undermining the reach and influence of the Blacklist system.
Why does the West see the system so differently?
Myths of the SCSP need not only debunking but also careful analysis. With Marianne von Blomberg, I analyze how US media use the “SCS imaginary” as a form of techno-orientalism — portraying a high-tech Asian “Other” as a threat to Western values. This narrative often serves as a rhetorical mirror for Western audiences to articulate their own domestic anxieties about technology and control rather than reflecting the ground-level reality in China.
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2026
The portrayal of China's Social Credit System (SCS) as an Orwellian scoring scheme through which the Communist Party controls citizens' every step proliferates in Western media, despite scholarship pointing out that it has little in common with the realities of SCSs in China. The imagined techno-dystopia informs policymaking, achieved meme status as it is invoked in broader debates around governance, technology, and geopolitics. Based on sociology of media, literature studies, and postcolonial theories, we argue that the SCS imaginary is indicative of Techno-orientalism, which supplements traditional Orientalism by emphasizing how a highly technologized Asian Other threatens the West. Previously only observed in works of fiction, we demonstrate how Techno-orientalism permeates fact-based news reporting, too. We analyze 405 SCS-related media articles published in the U.S. from 2002 to 2023 with regard to how the Chinese SCS is imagined, constructed, and deployed. The imagined techno-orient is useful: As a marker of the authoritarian threat in the digital age, it serves to reassure U.S. audiences that their own institutions are superior. As constructed case for technology gone bad, the SCS imaginary assists the U.S. debate to articulate own fears of losing control over technology, perceived authoritarian tendencies in domestic politics and the challenge of China's rise to the global order.
Metricocracy
Currently, I am writing a book, Metricocracy, an ethnography of a Chinese social credit system. It is under contract with University of California Press.
Metricocracy (metric-ocracy, rule by metric) offers an unprecedented ethnographic account of China’s social credit system — not as a dystopian surveillance apparatus, but as a fragile, fragmented, and often performative bureaucratic project. Based on extensive fieldwork in “Meritown,” a northern Chinese city that pioneered one of the country’s most ambitious local credit systems, the book reveals how quantification intended to promote behavioral compliance and bureaucratic oversight ends up producing selective, fabricated, and ultimately mirage-like data. Through close observation of the daily work of street-level bureaucrats and their interactions with citizens, Metricocracy traces how numbers are negotiated, manipulated, and invested with conflicting meanings, often at odds with the state’s official narrative of comprehensive social governance.
Rather than reinforcing state power, the book shows how quantification can generate institutional strain, symbolic contestation, and even cynicism among bureaucrats and citizens alike. By illuminating the political, organizational, and relational dynamics behind the production of scores, Metricocracy challenges dominant accounts of China’s authoritarian capacity and adds new depth to global debates on data-driven algorithmic governance, quantification, datafication, and state legitimacy.