The State's Politics of "Fake Data"
Chuncheng Liu, danah boyd
TL;DR
This paper challenges the assumption that state data must perfectly reflect reality, arguing that 'fake data' are a relational, processual, and performative phenomenon produced along bureaucratic pipelines. Through ethnographic studies of Chinese street-level volunteering data and the U.S. Census, the authors identify four moments of fakeness—creation, correction, collusion, and augmentation—and show how data are valued for their utility rather than their representational accuracy. They propose contextual data governance and uncertainty-forward system design to render the politics of data fictions legible, contestable, and accountable. The findings have practical implications for policymakers, designers, and data users to manage uncertainty and maintain democratic accountability in data-driven governance.
Abstract
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.