States wield symbolic power to legitimize the data they produce, utilize, and disseminate. In turn, state data acts to affirm the legitimacy of the state itself. Within this dynamic, much scholarly attention has been directed at the importance of genuine data production, emphasizing its representational accuracy. However, the meaning and production of 'fake data,' commonly perceived as either technically erroneous (wrong data) or normatively undesirable (bad data), have been somewhat overlooked. This paper challenges this predominantly representationalist perspective on fake data, drawing on ethnographic, interview, and archival data from two distinct cases where fake data controversies arose. The first delves into the methods employed by Chinese bureaucrats in documenting neighborhood issues and activities. The second scrutinizes a specific facet of the United States Census Bureau's census data production. Rather than accepting the notion of fake data as an inherent, independent, and immutable entity, we encourage scholars to transcend debates centered on data's representational aspects. Instead, we advocate for an exploration of how people do things with fake data throughout its social trajectories. In mapping the ways states conceptualize, interpret, and navigate fake data, we highlight how—from its inception to its rectification and interpretation—multiple actors shape and redefine it at every phase, imbuing it with diverse meanings that often transcend mere representation of reality. Consequently, designating data as 'fake' goes beyond mere acknowledgment of its representational shortcomings. It's a sociopolitical act that demands a nuanced, context-rich analysis.
To govern, states collect and evaluate information about citizens, extracting numbers from social realities. Situating it within the sociological literature of state, infrastructures, and quantification, this paper describes the complexities of a Social Credit System (SCS) in China, a state initiative aimed at governing trust through quantification of social behavior. Adopting the ethnography of infrastructure, our analysis opens the “black box” of an SCS metric, investigating how trust is translated into a quantifiable measure and the implications of this translation. The study reveals that while the SCS is designed to build trust and accountability through a form of mechanical objectivity, it is deeply relational, 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 compared to their urban counterparts. 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.
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.
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.
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.