The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.
Micro-work, artificial intelligence and the automotive industry
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In addition, all companies with AI products require their data annotated and the cost and labour-intensive nature of this process results in the majority of these tasks being outsourced to external providers (Tubaro and Casilli 2019).”
From The poverty of ethical AI: impact sourcing and AI supply chains (2025) · cites this paper as Tubaro and Casilli 2019
In the citing paper
“As sociologists of technological innovation highlight, a lot of human labour is needed to fill the cracks of AI (Gray and Suri 2019;Tubaro and Casilli 2019;Tubaro et al. 2020).”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Tubaro and Casilli 2019
Citations
Cites
(1993) · Journal of the American Dietetic Association · doi:10.1016/0002-8223(93)91697-o · Cited by 18,452
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
Platform Competition in Two-Sided Markets
Rochet, J.-C., & Tirole, J. (2003) · Journal of the European Economic Association · doi:10.1162/154247603322493212 · Cited by 5,642
Braverman, H. (1974) · Monthly Review · doi:10.14452/mr-026-03-1974-07_1 · Cited by 5,505
The second machine age: work, progress, and prosperity in a time of brilliant technologies
(2015) · Choice Reviews Online · doi:10.5860/choice.184834 · Cited by 4,409
On the Principles of Political Economy, and Taxation
Ricardo, D. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781107589421 · Cited by 4,213
Why Are There Still So Many Jobs? The History and Future of Workplace Automation
Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917 · cited 1×
“…policymakers have echoed the dire job loss predictions of a few prospective studies (Brynjolfsson and McAfee 2014; Frey and Osborne 2017), although historical evidence suggests that dynamics of complementarity rather than substitution between labor and machinery might also be in place (Autor 2015).”
BackgroundLabor and Monopoly Capital: The Degradation of Work in the Twentieth Century.
Tomich, D., & Braverman, H. (1976) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063071 · Cited by 2,999
Layers of Silence, Arenas of Voice: The Ecology of Visible and Invisible Work
Star, S. L., & Strauss, A. (1999) · Computer Supported Cooperative Work (CSCW) · doi:10.1023/a:1008651105359 · Cited by 1,500
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies.
West, J. C. (2015) · Psychiatry · doi:10.1080/00332747.2015.1105632 · Cited by 1,423
Research works printed but not linked: 1
- Irani, L. (2016). The labor that makes AI “magic”. White House and NYU: AINow Summit.
Cited by
Sorted by citations
Items 101–122 of 122
Sprenger, F. (2021) · Digitale Gesellschaft · doi:10.14361/9783839450246 · Cited by 1
Lazega, E. (2020) · Edward Elgar Publishing eBooks · doi:10.4337/9781839102370.00016
Arendt in the Platformised World: Labour, Work and Action on Digital Platforms
Charlton‐Czaplicki, T. (2023) · doi:10.22439/phd.24.2023
AI Adoption in Automotive R&D: A Case Study Method for Prioritization of Inhibitors
Reddy, R. C., Mishra, D., Goyal, D., & Rana, N. P. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-50192-0_3
Sensing Decisions: Perceiving, Classifying, Finessing
Hind, S. (2024) · Springer eBooks · doi:10.1007/978-981-97-1749-1_4
Training Decisions: Ground-Truthing the Interesting
Hind, S. (2024) · Springer eBooks · doi:10.1007/978-981-97-1749-1_3
Feature Learning using Deep Variational Autoencoder for Prediction of Defects in Car Engine
Susanto, N., & Pardede, H. F. (2024) · International Conference on Information Technology Research and Innovation (ICITRI) · doi:10.1109/icitri62858.2024.10699115
Moving Beyond Haves and Have-Nots
Han, N., & Siegel, E. (2025) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0050 · Cited by 1
VR and AR Technologies Integrated Intelligent Transport System
Jadhav, S. G., Jahan, N. Z., Karthiga, S., Punitha, A., Neeraja, K. N., & Kannan, R. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-7287-6.ch005
Virtual Prototyping in the Automotive Industry
Kumar, S., Ananth, J., Manikandan, N., Karthik, K., Vijayaraghavan, P., & Sudhakar, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-7287-6.ch002
Artificial intelligence and creativity: the ethical debate
Vinchon, F., Rolland, F., & Lubart, T. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34073-4.00017-4
El-Kady, R. M. (2025) · Contributions to management science · doi:10.1007/978-3-032-02526-5_20
Gonthier, N. (2021) · theses.fr (ABES) · doi:10.70675/9abc66b4zca61z423bz85dcz07b849b15209
Elambert, J. (2022) · theses.fr (ABES) · doi:10.70675/5a29908cz9b0fz4d26za729zf767449aedbd
Développer des machines parlantes : les chatbots à l’épreuve du marché
Esteban, C. (2022) · theses.fr (ABES) · doi:10.70675/5d8e685cz5a3bz4f85z87e6z5a514d3e4a1f
O MICROTRABALHO E MÉTODOS DE TREINAMENTO DE INTELIGÊNCIA ARTIFICIAL
Oliviero, M., Sobrinho, L. L. P., & Basigli, N. (2025) · Novos Estudos Jurídicos · doi:10.14210/nej.v30n3.p334-349
Üretken Yapay Zekâ Çağında Çalışmanın Geleceği Nedir? Marksist ve Ricardocu Bir Analiz
Eren, E. (2026) · 4 BOYUT Medya ve Kültürel Çalışmalar Dergisi / 4 BOYUT Journal of Media and Cultural Studies · doi:10.26650/4boyut.2026.1953989
Hafeez-ur-Rehman, & Ferraro, A. V. (2026) · International Journal of Emerging Multidisciplinaries Social Science · doi:10.54938/ijemdss.2026.05.1.684
YURTSEVER, A. E. (2026) · Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi · doi:10.18493/kmusekad.1655005
Wu, T., & Xia, B. (2026) · De Gruyter eBooks · doi:10.1515/9783111373096-024
Metadata
- Authors
- Tubaro, P., & Casilli, A. A.
- Affiliations
- Université Paris-Sud (FR)
- Télécom Paris (FR)
- Corresponding author
- Tubaro, Paola
- Venue
- Journal of Industrial and Business Economics
- Published
- 2019-06-05
- Volume
- 46
- Issue
- 3
- Pages
- 333-345
- ISSN
- 0391-2078 (ISSN-L)
- 1972-4977
- OpenAlex
- W2948141579
- Cited by
- 122
- References
- 26
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 24 · 2023: 16 · 2022: 20 · 2021: 21 · 2020: 16 · 2019: 3
- FWCI
- 30.21
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- Journal of Industrial and Business Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Force Ouvrière-IRES (2017-3)
- France Stratégie (2018)
- MSH Paris Saclay (17MA07)
- Topics
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Social Sciences › Business, Management and Accounting › Strategy and Management › Global trade, sustainability, and social impact
- Keywords
- artificial intelligence, data annotation, low-skilled labor, automotive industry, autonomous vehicles, connected vehicles, AI training data, digital platforms, global supply chains, gig economy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review