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Problematizing Learning: On the Labor Process and Knowledge Extractivism in Making Data Workers for AI in China

18 April 2023 (Tue)

4:30-6:00 p.m.

LBY G01, Lingnan University


Registration link:

A confirmation email will be sent on or before April 17 (Mon).

Human labor to annotate data is integral to the development of artificial intelligence (AI). While studies showed data analytic professionals learn to develop a “data vision,” less attention has been paid to how workers learn to work for AI because data annotation is often perceived as jobs requiring minimum skills. Using the case of Chinese data workers, this talk rethinks and theorizes the relations between learning and labor in data production for AI. I will demonstrate, first, how constant learning and practice have become intertwined in the labor process of data production in China. Then I will discuss how consent is molded and ambiguity resolved through learning to labor. Lastly, I will argue why the moments of questioning and confusion represent possibility of negotiation. Conceptualizing learning as labor, consent manufactured through learning, and unlearning as resistance, the talk will conclude with a discussion on the contradictions in the making of data workers in China and their implications for the datafied society.

Prof. Julie Yujie Chen the Assistant Professor at the Institute of Communication, Culture, Information, and Technology (ICCIT) at the University of Toronto. Her latest publications include Zhang, L. & Chen, J.Y. (2022). “A Regional and Historical Approach to Platform Capitalism: The Cases of Alibaba and Tencent.” Media Culture &Society; Andrijasevic, R., Chen J.Y., Gregg, M., & Steinberg, M. (2021). Media and Management, University of Minnesota Press, and Doorn, N. & Chen, JY. (2021). “Odds against workers: datafied gamification on Chinese and American food delivery platforms.” Socio-Economic Review.

If you have any enquiries, please contact Ms. Jolie Chan at [email protected] or 2616 7696.