Surveillance Creep in Times of Crisis: The Alipay Health Code and Implications for Privacy, Civil Liberties, and Social Control
Bibliographic record
Abstract
A new type of coronavirus disease, known as Covid-19, originated in Wuhan, Hubei, China, in late December 2019.This disease, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulted in severe pneumonia in some individuals.With its high infectivity and ability to spread between humans, Covid-19 quickly spread across China and eventually evolved into a global pandemic affecting countries worldwide.What accompanied the spread of the virus was a wave of panic.Starting from China, governments around the world tried to come up with various coping strategies, including but not limited to the development of vaccines, mass quarantine, restriction on traveling, city lockdown, and the implementation of large-scale surveillance.Among all these methods, the Alipay Health Code system has been able to achieve significant success in regulating people's movement and reducing the risk of contagions.This is a digital health tracking system created by Ant Financial Services Group, affiliated with Alibaba Group, as a response to the Covid-19 pandemic.It uses a color-coded QR code (red, yellow, or green) to indicate an individual's health status and their potential risk of spreading Covid-19.To obtain the code, users must complete a health declaration questionnaire through the Alipay app, sharing details about their travel history, current health condition, and potential exposure to the Abstract: This paper illustrates the ways in which governments can infringe upon civil liberties and expand surveillance measures in response to a crisis, through employing technological determinism as a vehicle to examine the Alipay Health Code system.The system was developed by Alipay and introduced in China in 2020 in response to the Covid-19 crisis to help monitor and control the spread of the virus, but was heavily criticized on social media by many Chinese people and was eventually scrapped in December 2022.It argues that surveillance technologies are inherently deterministic as they shape societal structures and power dynamics, often reinforcing existing hierarchies and inequalities.While social constructivism could be employed to analyze surveillance technologies, many stakeholders are not able to actively participate in shaping the discourse, utilization, and governance of these technologies due to limited access to information and power differentials, and the technological "black box" makes it challenging for citizens to fully understand and influence the decision-making processes and algorithms that govern their lives.It further argues that it is inevitable that the government will use another crisis to implement a surveillance system that expands its monitoring capabilities and further encroaches on individual privacy in the future.As a society, we must decide how to balance the need for public safety with the protection of civil liberties, ensuring transparency, accountability, and citizen participation in shaping the deployment and governance of such systems so that the deterministic nature of these technologies is tempered by ethical considerations and safeguards to prevent abuses of power.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.034 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".