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ChinAI #230: Women Raising AI and Babies
Data labeling and women's work in rural China
Greetings from a world where…
the Kenergy is off the charts
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Feature Translation: On the Loess Plateau, women who have not gone to college are raising AI
Context: In the Guangzhou-based magazine 南风窗 (South Reviews), Qiuyu Zhu provides a longform report (11 pages & 4,000 words) on AI-driven demand for data labeling and women’s work in rural China.
Key People: Cao Yali and Liu Xia
Cao Yali thinks that, among her and her two siblings, she “did the worst” as the only one who did not attend college. In 2019, she left Xi’an to return to her parents’ home in Qingjian County, Shaanxi Province. Qingjian is known for producing red dates (a prune-like fruit), but the effects of climate change have devastated crop yields. As a result, young people have left for Yulin, Xi'an and other places to seek their futures.
Cao Yali, at age 30, went the opposite direction. After returning to her hometown, she struggled to find a job for about a year. In 2020, she became an “AI trainer” at Qingjian Aidou, a data labeling company. On a good day, if she exerts herself and works overtime, she can review 30,000 images a day.
Liu Xia is from nearby Yonghe County. She had top marks in high school, but none of her siblings went to college, and her parents didn’t value further education. So she moved to a city and worked in sales at a clothing store. At age 25, feeling the pressure from her relatives about starting a family, she returned to her hometown and got married.
One year into marriage, she became pregnant and quit her job, eventually leaving the workforce for three years. In Yonghe County, most jobs come from the service industry, and it was unrealistic for Liu Xia to work 7 days a week and take care of the baby.
In 2020, the Yonghe branch of Qingjian Aidou started recruiting for data labelers. The advert clearly stated, “two days off on the weekends.” This made Liu Xia, who was in her postpartum period after having another kid, contemplate returning to work. She noticed that several mothers had posted about working at data labeling companies in their WeChat status updates. Specifically, each of them had received a rose on International Women’s Day. In seven years of marriage, she had not received a single gift from her husband.
*At the Yonghe data labeling center, 128 of 133 employees are women. For more statistics on data labeling services, the AI Dou initiative to boost digital employment in county towns, and details on the changing requirements of data labeling in China, see FULL TRANSLATION: On the Loess Plateau, women who have not gone to college are raising AI
ChinAI Links (Four to Forward)
Should-Listen: BBC Unexpected Elements
Last month, Yangyang Cheng, a researcher at Yale Law School, presented a story about data labelers in Chinese county towns for this “Under the Radar” segment of a new BBC science show. Her presentation was based on ChinAI #223, which translated a data annotator’s perspective from a small county town in northwest China.
Must-read: China’s AI Regulations and How They Get Made
An excellent Carnegie Endowment for International Peace paper by Matt Sheehan on China’s approach to AI regulation: “In this series of three papers, I will attempt to reverse engineer Chinese AI governance. I break down the regulations into their component parts—the terminology, key concepts, and specific requirements—and then trace those components to their roots, revealing how Chinese academics, bureaucrats, and journalists shaped the regulations.”
For The Washington Post, Meaghan Tobin breaks down why China lags behind the U.S. in AI. Includes some comments from me and cites my recent GovAI report, co-authored with Jenny Xiao, on Chinese large language models.
Should-read: Your employer is (probably) unprepared for AI
The Economist enters the debate over when AI will make a substantial impact on national economies. Cites my recent diffusion deficit paper.
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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