The Health Code System: or the Reinforcement of New Digital Mass Surveillance Practices
The rapid acceleration in the adoption of new technologies during infectious disease surveillance is not new, nor is it only applicable to the contexts of Covid-19. However, the outbreak of the coronavirus has brought a reality that demanded a quick and effective pandemic response. With the disappearing line between surveillance and public control, big data was put to the forefront as a means to keep the spread of the novel coronavirus under control.
The new technology-led solutions – employed to aid the response towards Covid-19 – sparked debates that were underpinned by either “utopian or dystopian rhetoric” (Boyd & Crawford 2012, 663). On the one hand, data emerged as a “new superpower” utilized to address the pandemic-related challenges (Zuckerberg 2020). Meanwhile, the “far from ideal” purpose of such solutions were questioned as they may not be “fit-for-purpose” (Kitchin 2020, 368). By drawing on such assessments, I conceptualize data (Kitchin 2014) through the analysis of China’s Health Code system to determine the how the application of big data during the coronavirus pandemic tested the ground for new mass surveillance tools?
Mass Surveillance through Coloured QR Codes
Overview of the Health Code System
With the global outbreak of the Covid-19, China began searching among the myriad of ways how to best employ the advantages offered by digital technologies and big data to “early detect the coronavirus and enhance the precision of antiepidemic work” (Cong 2021, 3). As a result, the Health Code – one out of the many initiatives – has been employed to follow the overall strategy offered by the government of China.
First developed by the tech giant Alibaba and later followed by Tencent, in early February 2020 China has introduced its approach on how to combat the spread of the virus through the coloured QR codes. The Health Code, embedded in Alipay (owned by Alibaba) and WeChat (operated by Tencent) via a mini-app (Cong 2021, 4), employes big data approaches to monitor and collect data concerning people’s Covid status, and to track their movement across the country (Bernot et al. 2021).
Functionality wise, the Health Code operates through the collection and analysis of three types of data to determine one’s contagious risk (Mozur et al. 2020). Liang (2020) offers a further classification of these data sources into the following categories:
- The first type is the collection of user’s personal information such as name, national ID number, home address, physical condition;
- The second set addresses spatial-temporal data recorded by apps to determine whether a user has visited a high-risk area and the duration of their trip;
- The final category tracks user’s “networks and online transactions” (1) to analyse if they have been in contact with anyone infected by the Covid-19.
After the data is processed, the program evaluates the user’s health status by assigning one of the three coloured QR codes unique for each person. The green code allows users to travel without any restrictions; those who have received a yellow code have to quarantine for one week, while people with a red code need to undergo a two-week quarantine (Cong 2021, 4).
The Mass Surveillance Dilemma
On the surface level, the Health Code system sets a template of a powerful tool ready to address the pandemic-caused challenges. However, things are not as simple as they may seem. It was revealed that the information gained through the system appears to be shared with the police (Mozur et al. 2020). By looking at this matter via the “technical” aspect of the data (Kitchin 2014) the Health Code appears as a “troubling manifestation of Big Brother” (Boyd & Crawford 2012, 664), or in other formulation a man-powered mass surveillance project, that tracks and dictates its users’ lifestyle.
It should be noted that at a certain point, the creep of the digital surveillance reached the point that the health officials from Hangzhou even proposed to not only track citizens’ daily activities but introduced a new “ranking” system to give scores based on their lifestyle habits – drinking, exercising, smoking, etc. (Gan 2020). The controversial proposal was later deleted from the official website as it was met with public backlash, however, the original text can be still accessed through the Internet Archive.
Besides, it is equally important to reflect on the ethical perspectives that underline the Big Data phenomenon. Kitchin (2014) argues that nations have to enact legislation to protect data. However, with no global rule of the game, countries operate according to their regulations, further deepening the issues of the ethical aspect of data collection, privacy and power abuse.
As a response to the critical reflection on big data, it is crucial to determine how the availability of the data, its procession, analysis and storage has radically transformed over time (Kitchin 2014). And, although digital and data literacy can be argued to have improved, we still find ourselves unable to resist and yet provide access to our data to use it under the legitimized label of “public good” (such as during the use of Health Code system).
Throughout this period, we got to see the employment of the many already in use technologies with the application of new features. What has changed is perhaps the increase in the scale of the digital surveillance and power control that the governments and the tech giants have exercised on us. Kitchin (2014) asserts that data serves as the fundamental part of knowledge production. With respect to this, it is worth questioning what to expect out of all the accumulated knowledge and experience collected though this period, and in what new ways will our data be applied to harness new digital mass surveillance practices.
Bernot, A., Trauth-Goik, A., & Trevaskes, S. 2021. “China’s ‘Surveillance Creep’: How Big Data Covid Monitoring could be used to Control People Post-Pandemic.” The Conversation. 31 August, 2021. https://theconversation.com/chinas-surveillance-creep-how-big-data-covid-monitoring-could-be-used-to-control-people-post-pandemic-164788
Boyd, D., & Crawford, K. 2012. “Critical Questions for Big Data.” Information, Communication & Society, 15(5), pp. 662–679.
Cong, W. 2021. “From Pandemic Control to Data-Driven Governance: The Case of China’s Health Code.” Front. Polit. Sci. 3:627959. doi:10.3389/fpos.2021.627959
Gan, N. 2020. “With the Coronavirus Under Control, this Chinese City Wants to Score and Rank its Residents Based on their Health and Lifestyle.” CNN Business. 26 May, 2020. https://edition.cnn.com/2020/05/25/tech/hangzhou-health-app-intl-hnk/index.html
Kitchin, R. 2014. “The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences.” Thousand Oaks, CA: SAGE Publications Ltd. Chapter 1: Conceptualising Data.
Kitchin, R. 2020. “Civil Liberties or Public Health, or Civil Liberties and Public Health? Using Surveillance Technologies to Tackle the Spread of COVID-19.” Space & polity 24, no. 3 (2020): 362–381.
Liang, F. 2020 “COVID-19 and Health Code: How Digital Platforms Tackle the Pandemic in China.” Social Media + Society. https://doi.org/10.1177/2056305120947657
Mozur, P., Zong, R., & Krolik, A. 2020. “In Coronavirus Fight, China Gives Citizens a Color Code, With Red Flags.” The New York Times. 1 March 2020. https://www.nytimes.com/2020/03/01/business/china-coronavirus-surveillance.html
Zuckerberg, M. 2020. “Here’s an op-ed I Wrote about the Symptom Survey Maps We’re Releasing Today.” Facebook, April 20, 2020. https://www.facebook.com/zuck/posts/heres-an-op-ed-i-wrote-about-the-symptom-survey-maps-were-releasing-today-that-l/10111823797526771/