ChinAI #203: A Critique of Health Codes as the Digital Leviathan
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Feature Translation: Have you made preparations to use "health codes" all the time?
Context: Originally authored by Houchen Li in April 2020, this week’s feature translation is an incisive critique of the initial rollout of health codes in China during the early days of Covid control. Following a recent announcement that China will build out digital health codes into a national health information platform, I’ve seen Li’s article (link to original Chinese) resurface in some WeChat groups. It was shared on Vistopia [看理想], a publishing company that also produces podcasts and video content, specializing in literary, art, and cultural criticism.
Key Takeaways: Li warns against the normalization of health codes, which stand out as the first measure that combines three features: mobile phone interface + 3D face recognition + multi-scenario population management.
Li’s fear is that governments use the pandemic as an opportunity to strengthen population management via health codes. “At that time, ‘the health of the body’ will have a broader meaning and become ‘social health’ in a larger sense, truly making it so that unhealthy people will find it ‘difficult to walk an inch’,” Li writes.
One historical reference is subway security checks. “Before 2008, you only needed to buy a ticket to take the subway, and the ticket was completely separated from the personal identity of the person. After the Beijing Olympics, the subway security measures that were escalated during the Olympics became normalized, and the same happened for Shanghai after the 2010 World Expo. For example, although Guangzhou canceled the security check after the 2010 Asian Games, most Chinese cities have normalized subway security checks in 2014 and 2015.”
The article comes out strongly against the logic of “better to overdo it than not do enough” when it comes to containing Covid risks, highlighting the disproportionate negative effects of health codes on high-risk groups.
Identification and control of high-risk populations leads to desperate calls for a digital Leviathan to intervene administratively in a way that “goes over the limit.” Li states, “This is exactly the same as our logic of isolating Hubei people, medical staff, and overseas returnees, and it has repeatedly reminded us of our deep-rooted hypocrisy.”
I found this passage especially compelling: “I am not a person with a rich imagination. As long as we adhere to the principle of ‘overdoing is always better than not doing enough’, then high-risk groups will always have the moral flaw of ‘insufficient self-discipline,’ and we will invent far more ways to limit them.”
What can people do to contain the digital Leviathan?
Acknowledging the inevitability of health codes, the path forward is to prod the system in a way that protects humans as the bottom line.
Article contains three main points: a) urge public disclose of the health code system’s specific algorithms and scoring rules; b) ensure that there is an appeal process that compels administrators to respond to unfair code judgements; c) compensation for high-risk groups, in order to prevent the health code from becoming a tool of discrimination.
FULL TRANSLATION: Have you made preparations to use "health codes" all the time? *I had meant to translate some of the top comments from the WeChat public account. Bonus points to any ChinAI readers who can add some translated comments to the bottom of the Google Doc (this requires you to identify the WeChat public account, find the original article via phone, and then scroll down to the comments).
ChinAI Links (Four to Forward)
Should-read: China’s AI Workforce: Assessing Demand for AI Talent
First CSET report on China’s AI workforce: “U.S. policies on artificial intelligence education and the AI workforce must grow, cultivate, attract, and retain the world’s best and brightest. Given China’s role as a producer of AI talent, understanding its AI workforce could provide important insight. This report provides an analysis of the AI workforce demand in China using a novel dataset of 6.8 million job postings. It then outlines potential implications along with future reports in this series.”
Ryan McMorrow and Cheng Leng’s had an excellent Financial Times article back in June 2022, with details on the mechanics of these health codes:
This month, a joint venture between [Alibaba] and two state-owned firms won a 12-month contract to run the system, which is required to be robust enough to handle 25,000 information queries per second. The records show Hangzhou’s 12mn residents are separated into several data sets, each with different rules. One data set for workers in the delivery and cold chain logistics sectors ensures they are given an orange health code for skipping a Covid-19 test. But a “white list” for pandemic workers and other “special groups” has instructions to protect them from getting orange or red codes while they are carrying out their duties.
Matt Sheehan and Sharon Du, for Carnegie Endowment for International Peace, have compiled a brief case study that looks at “how Chinese food delivery drivers, investigative journalists, and academics helped shape one part of the world’s first regulations on recommendation algorithms.”
I’m quoted in this Protocol piece on the tension between U.S.-China competition and the open-source software movement. This article is part of Kate Kaye’s important series on the question: “Are the U.S. and China really in an AI race?”
Thank you for reading and engaging.
These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.
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