Politics of algorithms

Algorithms in the Covid-19

Making Sense of Algorithms: Relational Perception of Contact Tracing and Risk Assessment during the Covid-19. Big Data & Society. 2021. 18(1). (with Ross Graham) |download| |abstract|

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.

Seeing Like a State, Enacting Like an Algorithm: (Re)assembling Contact Tracing and Risk Assessment during COVID-19. Science, Technology & Human Values. forthcoming. |download| |abstract|

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.

Chinese social credit systems

Trusting by Numbers: An Analysis of a Chinese Municipal Social Credit System Infrastructure. in progress (with Akos Rona-Tas) |abstract|

Both states and markets collect and evaluate information about people to enact governance and control through various institutions and infrastructures, many of which simplify social realities into numbers. This study examines the infrastructure of a Chinese municipal social credit system (SCS) that aims to collect people’s data of trustworthiness and produces credit scores, pursuing a state goal with a market approach. Through the unpacking and reconstruction of its rules, structure, and metric, we illustrate the SCS’s logic, hidden assumptions, and uneven focuses. We find that systematic biases exist in the system and would distort the representation of trustworthiness. The system unevenly impacts different populations in a nuanced way. While many disadvantaged groups could be further marginalized, some privileged social groups are more disciplined by the system in another way. We further discuss the implications of the SCS in the context of the expanding state and market algorithmic surveillance globally.

Multiple Social Credit Systems in China. Economic Sociology: the European Electronic Newsletter. 2019. 21(1): 22-32. |download| - |abstract|

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.

Scroll to top