This paper delves into the human factors in the “back-office” of artificial intelligence and of its data-intensive algorithmic underpinnings. We show that the production of AI is a labor-intensive process, which particularly needs the little-qualified, inconspicuous and low-paid contribution of “micro-workers” who annotate, tag, label, correct and sort the data that help to train and test smart solutions. We illustrate these ideas in the high-profile case of the automotive industry, one of the largest clients of digital data-related micro-working services, notably for the development of autonomous and connected cars. This case demonstrates how micro-work has a place in long supply chains, where tech companies compete with more traditional industry players. Our analysis indicates that the need for micro-work is not a transitory, but a structural one, bound to accompany the further development of the sector; and that its provision involves workers in different geographical and linguistic areas, requiring the joint study of multiple platforms operating at both global and local levels.
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Data used in this study are from the DiPLab (“Digital Platform Labor”) research project, co-funded by Maison des Sciences de l’Homme Paris-Saclay (2017); Force Ouvrière (2017), a workers’ union, through IRES (Social and Economic Research Institute); and France Stratégie (2018), a service of the French Prime Minister. We also thank Foule Factory and IsAHit for logistical support, and Inria for complementary funding.
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Tubaro, P., Casilli, A.A. Micro-work, artificial intelligence and the automotive industry. J. Ind. Bus. Econ. 46, 333–345 (2019). https://doi.org/10.1007/s40812-019-00121-1
- Artificial intelligence
- Automotive industry
- Digital platform economy
- Organization of work