Learners in the loop: hidden human skills in machine intelligence

Journal title SOCIOLOGIA DEL LAVORO
Author/s Paola Tubaro
Publishing Year 2022 Issue 2022/163 Language English
Pages 20 P. 110-129 File size 236 KB
DOI 10.3280/SL2022-163006
DOI is like a bar code for intellectual property: to have more infomation click here

Below, you can see the article first page

If you want to buy this article in PDF format, you can do it, following the instructions to buy download credits

Article preview

FrancoAngeli is member of Publishers International Linking Association, Inc (PILA), a not-for-profit association which run the CrossRef service enabling links to and from online scholarly content.

Today’s artificial intelligence, largely based on data-intensive machine learning algorithms, relies heavily on the digital labour of invisibilized and precarized humans-in-the-loop who perform multiple functions of data preparation, verification of results, and even impersonation when algorithms fail. Using original quantitative and qualitative data, the present article shows that these workers are highly educated, engage significant (sometimes advanced) skills in their activity, and earnestly learn alongside machines. However, the loop is one in which human workers are at a disadvantage as they experience systematic misrecognition of the value of their competencies and of their contributions to technology, the economy, and ultimately society. This situation hinders negotiations with companies, shifts power away from workers, and challenges the traditional balancing role of the salary institution.

Keywords: Digital labour platforms, Artificial intelligence, Skills, Spanish-speaking countries

  1. Altenried M. (2020). The platform as factory: Crowdwork and the hidden labour behind artificial intelligence. Capital & Class, 44(2): 145-158. DOI: 10.1177/030981681989941
  2. Berg J., Furrer M., Harmon E., Rani U., Silberman M.S. (2018). Digital labour platforms and the future of work: Towards decent work in the online world. Geneva: ILO Report. -- Available at: www.ilo.org/global/publications/books/WCMS_645337/lang—en/index.htm.
  3. Casilli A.A. (2019). En attendant les robots: Enquête sur le travail du clic. Paris: Seuil.
  4. Casilli A.A., Tubaro P., Le Ludec C., Coville M., Besenval M., Mouhtare T.. Wahal E. (2019). Le micro-travail en France. Derrière l’automatisation, de nouvelles précarités au travail? Paris, Report of the Digital Platform Labor (DiPLab) project. -- Available at: https://hal.inria.fr/hal-02139528/.
  5. Chicchi F. (2020). Beyond the ‘salary institution’: on the ‘society of performance’ and the platformisation of the employment relationship. Work Organisation, Labour & Globalisation, 14(1): 15-31.
  6. Cognilytica (2020). Data preparation and labeling for AI 2020. Report CGR-DLP20. Washington DC: Cognilytica.
  7. Crouch C. (1997). Skills-based full employment: the latest philosopher’s stone. British Journal of Industrial Relations, 35(3): 367-391.
  8. Crouch C., Finegold D., Sako M. (2001). Are Skills the Answer? The Political Economy of Skill Creation in Advanced Industrial Countries. Oxford: Oxford University Press.
  9. Newlands G. (2021). Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service. Big Data and Society, 8(1). DOI: 10.1177/2053951721101602
  10. Palmer R. (2017). Jobs and skills mismatch in the informal economy. Geneva: International Labour Organization.
  11. Posada J. (2022, online first). Embedded reproduction in platform data work. Information, Communication & Society. DOI: 10.1080/1369118X.2022.2049849
  12. Schmidt F. (2019). Crowdproduktion von Trainingsdaten: Zur Rolle von Online-Arbeit beim Trainieren autonomer Fahrzeuge. Report, Düsseldorf: Hans-Böckler-Stiftung.
  13. Tomaskovic-Devey D., Avent-Holt D. (2019). Relational Inequalities: An Organizational Approach. Oxford: Oxford University Press.
  14. Tubaro P., Casilli A.A. (2019). Micro-work, artificial intelligence and the automotive industry. Journal of Industrial and Business Economics, 46(3): 333-345.
  15. Tubaro P., Casilli A.A., Coville M. (2020a). The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence. Big Data & Society, 7(1). DOI: 10.1177/205395172091977
  16. Tubaro P., Le Ludec C., Casilli A.A. (2020b). Counting ‘micro-workers’: societal and methodological challenges around new forms of labour. Work Organisation, Labour & Globalisation, 14(1), 67-82.
  17. Tubaro P. (2021). Disembedded or deeply embedded? A multi-level network analysis of online labour platforms. Sociology, 55(5): 927-944. DOI: 10.1177/003803852098608
  18. Tubaro P., Casilli A.A. (2022). Human listeners and virtual assistants: privacy and labor arbitrage in the production of smart technologies. In: F. Ferrari, M. Graham, eds., Digital Work in the Planetary Market, Cambridge (MA): MIT Press, pp. 175-190.
  19. Tubaro P., Coville M., Le Ludec C., Casilli A.A. (2022). Hidden inequalities: the gendered labour of women on micro-tasking platforms. Internet Policy Review, 11(1). DOI: 10.14763/2022.1.162
  20. UNESCO Institute for Statistics. (2022). Sustainable Development Goals Data. Available at: http://data.uis.unesco.org/.
  21. Wood A.J., Graham M., Lehdonvirta V., Hjorth I. (2019). Networked but commodified: The (dis)embeddedness of digital labour in the gig economy. Sociology, 53(5): 931-950. DOI: 10.1177/003803851982890
  22. Mothobi O., Gillwald A., Schoentgen A. (2018). What Is The State Of Microwork in Africa? A View from Seven Countries. Report, Research ICT Africa.
  23. Miceli M., Schuessler M., Yang T. (2020). Between subjectivity and imposition: Power dynamics in data annotation for computer vision, Proceedings of the ACM on Human-Computer Interaction, 4, CSCW2, 115, DOI: 10.1145/341518
  24. Mehrotra S. (2018). From the informal to the formal economy: Skills initiatives in India. In A. Sakamoto, J. Sung, eds., Skills and the future of work: Strategies for inclusive growth in Asia and the Pacific. Geneva: International Labour Organization, pp. 364-390.
  25. Mavridis P., Gross-Amblard D., Miklós Z. (2016). Using hierarchical skills for optimized task assignment in knowledge-intensive crowdsourcing. Proceedings of the 25th International Conference on World Wide Web, 843-853.
  26. Margaryan A., Charlton-Czaplicki T., Gadiraju U. (2020). Learning and Skill Development in Online Platform Work: Comparing Microworkers’ and Online Freelancers’ Practices. CBS Report.
  27. Margaryan A. (2019b). Workplace learning in crowdwork: Comparing microworkers’ and online freelancers’ practices. Journal of Workplace Learning, 31(4): 250-273. DOI: 10.1108/JWL-10-2018-012
  28. Margaryan A. (2019a). Comparing crowdworkers’ and conventional knowledge workers’ self-regulated learning strategies in the workplace. Human Computation, 6: 83-97.
  29. OECD (2021). Education at a Glance 2021. Report. Paris: OECD.
  30. Lindquist J. (2018). Illicit economies of the Internet: Click farming in Indonesia and beyond. Made in China Journal, 3(4): 88-91.
  31. Lehdonvirta V., Kässi O., Hjorth I., Barnard H., Graham M. (2019). The global platform economy: A new offshoring institution enabling emerging-economy microproviders. Journal of Management, 45(2): 567-599. DOI: 10.1177/014920631878678
  32. Kässi O., Lehdonvirta V., Stephany F. (2021). How many online workers are there in the world? A data-driven assessment [version 4; peer review: 4 approved]. Open Research Europe, 1:53.
  33. Irani L. (2015). The cultural work of microwork. New Media & Society, 17(5): 720-739. DOI: 10.1177/146144481351192
  34. Ipeirotis P. (2010). Demographics of Mechanical Turk. NYU Working Paper, CEDER-10-01.
  35. ILO (2021). World Employment and Social Outlook 2021: The role of digital labour platforms in transforming the world of work. Report. Geneva: ILO.
  36. Honneth A. 1995 [1992]. The Struggle for Recognition: The Moral Grammar of Social Conflicts. Cambridge: Polity Press.
  37. Heuer H., Jarke J., Breiter A. (2021). Machine learning in tutorials – Universal applicability, underinformed application, and other misconceptions. Big Data & Society, 8(1). DOI: 10.1177/20539517211017593
  38. Grohmann R., Fernandes Araújo W. (2021). Beyond Mechanical Turk: The work of Brazilians on global AI platforms. In: P. Verdegem, ed., AI for Everyone? Critical Perspectives, University of Westminster Press, pp. 247-266.
  39. Gray M., Suri S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Boston, MA: Houghton Mifflin Harcourt.
  40. Graham M., Anwar M. (2019). The global gig economy: towards a planetary labour market? First Monday, 24(4).
  41. ENCOVI (2021). Encuesta Nacional de Condiciones de Vida. Documento técnico. Caracas: Universidad Católica Andrés Bello.
  42. ENCOVI (2018). Encuesta Nacional de Condiciones de Vida. Caracas: Universidad Católica Andrés Bello.
  43. Ekbia H.R., Nardi B.A. (2017). Heteromation, and Other Stories of Computing and Capitalism. Cambridge (MA): MIT Press.
  44. Difallah D., Filatova E., Ipeirotis P. (2018). Demographics and dynamics of Mechanical Turk workers. Proceedings of WSDM 2018: the Eleventh ACM International Conference on Web Search and Data Mining, ACM: 135-143.
  45. Denton E., Hanna A., Amironesei R., Smart A., Nicole H. (2021). On the genealogy of machine learning datasets: A critical history of ImageNet. Big Data & Society, 8(2). DOI: 10.1177/20539517211035955

  • Complex thinking through a Transition Design-guided Ideathon: testing an AI platform on the topic of sharing economy Jorge Sanabria-Z, Isolda Margarita Castillo-Martínez, Laura Icela González-Pérez, María Soledad Ramírez-Montoya, in Frontiers in Education 1186731/2023
    DOI: 10.3389/feduc.2023.1186731

Paola Tubaro, Learners in the loop: hidden human skills in machine intelligence in "SOCIOLOGIA DEL LAVORO " 163/2022, pp 110-129, DOI: 10.3280/SL2022-163006