Research

Data have power. As such, most discussions of data presume that records should mirror some idealized ground truth. Deviations are viewed as failure. Drawing on two ethnographic studies of state data-making—in a Chinese street-level bureaucrat agency and at the US Census Bureau—we show how seemingly “fake” state data perform institutional work. We map four moments in which actors negotiate between representational accuracy and organizational imperatives: creation, correction, collusion, and augmentation. Bureaucrats routinely privilege what data do over what they represent, creating fictions that serve civil servants' self-interest and enable constrained administrations. We argue that “fakeness” of state data is relational (context dependent), processual (emerging through workflows), and performative (brought into being through labeling and practice). We urge practitioners to center fitness-for-purpose in assessments of data and contextual governance. Rather than chasing impossible representational accuracy, sociotechnical systems should render the politics of useful fictions visible, contestable, and accountable.

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. The oriental Other, re-empowered and updated for the AI era with the help of the SCS imaginary, is the rhetorical device of choice for engaging with abstract future fears.

How can “China” serve as a productive analytical tool for understanding the relationship between artificial intelligence’s and society? This commentary proposes a conceptual typology to answer that question, offering four ways to use China as an analytical lens – China as Mirror, China as Prototype, China as Counterpoint, and China as Global Actor. Each concept illustrates how China’s particularities and global entanglements can help reframe core sociotechnical questions about AI’s political economy, labor, subject formation, ideology, and global circulation. This typology contributes to the decolonization of knowledge production in critical AI studies, ensuring that insights are co-generated globally and avoiding essentialized boundaries that hinder this effort.

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.

Governance by quantification has a long history in human society. However, two critical issues remain underexplored in the sociology of quantification. First, while the social conditions and consequences of governance by numbers have been widely addressed, the politics of the ‘middle ground’ – quantification infrastructure itself, particularly with the rise of digitalization – are not fully elaborated. Second, the power dynamics that underpin and shape institutionalized quantification politics remain understudied, as numeracy and statistics are always preceded by political judgments about what to measure and how. This symposium advances discussions on governance by quantification infrastructure by offering a critical, reflective perspective on state quantification infrastructures and shedding new light on contested issues in the sociology of quantification globally.

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.

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.

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.

As states increasingly use algorithms to improve the legibility of society, particularly during the COVID-19 pandemic, it is common for concerns about the expanding power of the algorithm or the state to be raised in a deterministic manner. However, how are the algorithms for states’ legibility projects enacted, contested, and reconfigured? Drawing on interviews and media data, this study fills this gap by examining Health Code (jiankangma), the Chinese contact tracing and risk assessment algorithmic system that serves as the COVID-19 health passport. I first explore the intensive and invisible work and infrastructures that enact and stabilize Health Code’s sociotechnical assemblage. I then show how this assemblage is frequently challenged and destabilized by errors, breakdowns, and exclusions. Facing unintended engagements from heterogeneous social actors, local interests, and power hierarchies, Health Code reassembles into multiple and contradictory assemblages at different periods and social localities. Finally, I examine how people game and bypass the algorithm’s surveillance with their agencies. Recognizing this messiness and heterogeneity contributes to a more nuanced and realistic understanding of states’ use of algorithms, including the risks. Doing so also urges us to rethink the politics of citizenship and inequality in the digital age beyond inclusion.

Governments and citizens of nearly every nation have been compelled to respond to COVID-19. Many measures have been adopted, including contact tracing and risk assessment algorithms, whereby citizen whereabouts are monitored to trace contact with other infectious individuals in order to generate a risk status via algorithmic evaluation. Based on 38 in-depth interviews, we investigate how people make sense of Health Code (jiankangma), the Chinese contact tracing and risk assessment algorithmic sociotechnical assemblage. We probe how people accept or resist Health Code by examining their ongoing, dynamic, and relational interactions with it. Participants display a rich variety of attitudes towards privacy and surveillance, ranging from fatalism to the possibility of privacy to trade-offs for surveillance in exchange for public health, which is mediated by the perceived effectiveness of Health Code and changing views on the intentions of institutions who deploy it. We show how perceived competency varies not just on how well the technology works, but on the social and cultural enforcement of various non-technical aspects like quarantine, citizen data inputs, and cell reception. Furthermore, we illustrate how perceptions of Health Code are nested in people’s broader interpretations of disease control at the national and global level, and unexpectedly strengthen the Chinese authority’s legitimacy. None of the Chinese public, Health Code, or people’s perceptions toward Health Code are predetermined, fixed, or categorically consistent, but are co-constitutive and dynamic over time. We conclude with a theorization of a relational perception and methodological reflections to study algorithmic sociotechnical assemblages beyond COVID-19.

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.