Learners in the loop: hidden human skills in machine intelligence

Paola Tubaro

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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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