Learners in the loop: hidden human skills in machine intelligence

Paola Tubaro ()
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Paola Tubaro: CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique, LSQ - Laboratoire de sociologie quantitative - Centre de Recherche en Économie et STatistique (CREST), MSH Paris-Saclay - Maison des Sciences de l'Homme - Paris Saclay - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - ENS Paris Saclay - Ecole Normale Supérieure Paris-Saclay, LISN - Laboratoire Interdisciplinaire des Sciences du Numérique - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique, TAU - TAckling the Underspecified - LISN - Laboratoire Interdisciplinaire des Sciences du Numérique - Inria - Institut National de Recherche en Informatique et en Automatique - CentraleSupélec - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - Centre Inria de l'Université Paris-Saclay - Centre Inria de Saclay - Inria - Institut National de Recherche en Informatique et en Automatique

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Abstract: 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: misrecognition; Spanish-speaking countries; Digital labour platforms; artificial intelligence; skills; learning (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-big and nep-cmp
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Published in Sociologia del Lavoro, 2022, 163, pp.110-129. ⟨10.3280/SL2022-163006⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03787017

DOI: 10.3280/SL2022-163006

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