Research
My current research focuses on the politics of data-driven governance and quantification.
Research clusters
Recent publications
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2026
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.
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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.
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2025
Discussions of artificial intelligence (AI) in society have proliferated over the past decade. However, a significant portion of this discourse is anchored in Western contexts, often overlooking the rich, complex landscape of AI development and deployment in other parts of the world. This article proposes using China as an analytical lens to advance the broader field of AI and society. China's unique position as a major AI developer with distinct socio-political characteristics offers an invaluable perspective. Through a systematic review of existing AI research related to China and a discussion of China's specific context, we identify four key themes where China's experiences can challenge and enrich global AI discourse: data, labor, governance, and public perception. We argue that insights from China are not merely additive, but are fundamentally transformative in reshaping our understanding of AI's societal implications. This approach not only fills a significant gap in current AI research but also promotes a more nuanced, global understanding of AI's development and impact.
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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.