ChinAI #189: People Think Tank Survey | Public Perceptions of Face-Swiping in Smart Cities
Survey results by a think tank under the People's Daily (人民智库)
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Feature Translation: People Think Tank Report on Public Perceptions toward Face-Swiping in Smart Cities
Context: What does the Chinese public think about facial recognition in smart city applications? This week’s issue mines a first-time source: the People Think Tank (人民智库/renmin zhiku), a think tank affiliated with the People’s Daily (renmin ribao). The People’s Daily, as the authoritative newspaper of the Central Committee of the Chinese Communist Party, provides a good lens into China’s policy orientations. Leveraging The People’s Daily public opinion monitoring resources, the People Think Tank conducts large-scale surveys for the purpose of improving how the party governs the country.
This week’s feature translation looks at one such survey on facial recognition and smart cities. The results (link to original Chinese) were published earlier this month. Two interrelated points before we get into the results: 1) The think tank does not disclose their survey methodology and sample; 2) This means these results are significant not so much in a statistical sense (we don’t know if this is a reliable, representative survey) but more so in a sense that it sheds light into how Chinese policymakers interpret and frame public perceptions re: facial recognition and smart city applications.
A) Facial recognition applications have penetrated society, as 99.3 percent of respondents have had their face scanned in various scenarios, with the most popular being real-name registration for hotels and public buildings (51.8 percent) and entry to transportation stations (47.0%). As figure below shows, other popular applications include access control and attendance checks at companies and schools (43.9%), as well as entering and exiting at residential neighborhoods (33.5%).
B) The People Think Tank interprets the survey results to show that “the Chinese public has a high degree of acceptance of the application of face recognition technology,” which they contrast to the experience of Western countries where there has been resistance to the construction of smart cities.
The data cited: 70% of respondents are willing to use facial recognition, of which 40% of respondents are willing to use “face-swiping” in any scenario. Among respondents who have "face-swiped" to enter residential neighborhoods, 55% believe that smart access control can enhance security and prevent outsiders from entering.
Still, many of the survey findings could easily support the opposite conclusion: 30% of respondents were unwilling to use facial recognition, and 67% of the respondents believe that face recognition technology is being abused in current smart cities. In my opinion, the most revealing stat: “44.6% of the respondents believed that ‘face recognition, in and of itself, violates personal privacy and makes me feel like I'm being watched.’” Plus, about 42 percent of respondents expressed worries that their “personal whereabouts will be continuously recorded.”
One more fascinating tidbit: the report cites complaints about mandatory “face-swiping” in subways, residential neighborhoods, and park attractions made on the Message Board for Local Leaders (run by People’s Daily Online). For more details, see this Macro Polo piece, by Neil Thomas, which describes the board as a “fast-growing but under-studied” platform where netizens leave public messages for government bodies.
C) The People Think Tank report also criticizes excessive applications of facial recognition and acknowledges regulatory gaps. Here’s one section on avoiding over-reliance on facial recognition in smart cities:
“In particular, face recognition should be used with caution in the process of building smart residential neighborhoods. Sensors and target detection algorithms can be used to avoid infringing on the personal privacy of residents as much as possible... In public places such as subway stations and train stations, even if ‘face-swiping’ recognition is to be used, other identification methods should be reserved, and a singular mandatory ‘face-swiping’ method should not be adopted.”
It also calls out incidents that have sparked a lot of netizen backlash: “For example, schools use face recognition to monitor students' classroom behavior, and public toilets install ‘face recognition paper feeders’ to prevent the public from taking excessive amounts of paper, all of which run counter to the original intention of smart city development and also arouse public dissatisfaction” (emphasis mine).
Many more fascinating, detailed stats in FULL TRANSLATION: People Think Tank Report | “Face-swiping” Applications in Smart Cities: Public Perceptions and Evaluations
ChinAI Links (Four to Forward)
Must-read: AI with American values and Chinese characteristics: a comparative analysis of American and Chinese governmental AI policies
For AI & Society, Emmie Hine and Luciano Floridi have published an excellent article on how and why Chinese and American AI policies differ. Using NLP methods to compare U.S. and Chinese policy documents, they examine the factors that shape each country’s philosophy of technology and AI strategy. Special shout-out to Emmie, who I’m proud to say is a former student!
Yiqin Fu, a Stanford PhD student, penned an insightful thread on the need for more translations of work by Chinese academics:
Should-read: CSET’s policy.ai newsletter
Reading through this week’s CSET policy.ai newsletter, run by Alex Friedland, reminded me that this has become an essential round-up of AI policy news. Really great links to stories about restricting semiconductor exports to China, military prototypes of AI-enabled decision-making systems, and CSET’s report on China’s Paths to General AI.
Emily Jin, a research assistant at CNAS, wrote up a though-provoking thread on how the shift from engineers to lawyers in PRC leadership has shaped its approach to regulating digital currencies.
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 a postdoctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence.
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