Making Sense of Algorithms: Relational Perception of Contact Tracing and Risk Assessment during the Covid-19. Big Data & Society. 2021. (with Ross Graham) |Pre-print| |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.
Algorithms in Action: Reassembling Contact Tracing and Risk Assessment during the Covid-19. in progress. |Pre-print| |abstract|
During the Covid-19 pandemic, technologies such as contact tracing and risk assessment algorithms are widely used. While debates are heated about the optimal algorithm designs with respect to their effectiveness and ethics, little is known about how the algorithms are deployed, experienced, challenged, and reshaped in society. Combining in-depth interviews, media articles, and policy documents, this study examines how Health Code, the Chinese contact tracing and risk assessment algorithm, is assembled, disassembled, and reassembled in society. I argue for a conceptualization of algorithms as sociotechnical assemblages with the involvement of diverse human and non-human actors, which are constantly in action. I first explore the intensive and invisible work and infrastructures that enable Health Code to be enacted. However, these assembly attempts are consistently challenged in differing situations and destabilized Health Code from time to time. Health Code reassembles under the diverse yet unintended engagements of social actors, local networks, and power relations, which creates multiple Health Codes at different periods of time and social localities. I also examine how people game and bypass the algorithm’s surveillance as forms of everyday resistance. These findings go beyond the current technical debates and bring a more dynamic, nuanced, and realistic depicture of algorithms’ operation and power. Lastly, I explore how algorithms contribute to a new dialectical relationship between state and society, and how this relationship reshapes the mechanism of surveillance, inequality, and citizenship in this digital age.