Deskilling, Upskilling, and Reskilling: a Case for Hybrid Intelligence - Laboratoire Interdisciplinaire des Sciences du Numérique Access content directly
Journal Articles Morals & Machines Year : 2021

Deskilling, Upskilling, and Reskilling: a Case for Hybrid Intelligence

Abstract

Advances in AI technology affect knowledge work in diverse fields, including healthcare, engineering, and management. Although automation and machine support can increase efficiency and lower costs, it can also, as an unintended consequence, deskill workers, who lose valuable skills that would otherwise be maintained as part of their daily work. Such deskilling has a wide range of negative effects on multiple stakeholders –– employees, organizations, and society at large. This essay discusses deskilling in the age of AI on three levels - individual, organizational and societal. Deskilling is furthermore analyzed through the lens of four different levels of human-AI configurations and we argue that one of them, Hybrid Intelligence, could be particularly suitable to help manage the risk of deskilling human experts. Hybrid Intelligence system design and implementation can explicitly take such risks into account and instead foster upskilling of workers. Hybrid Intelligence may thus, in the long run, lower costs and improve performance and job satisfaction, as well as prevent management from creating unintended organization-wide deskilling.
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hal-04500314 , version 1 (11-03-2024)

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Janet Frances Rafner, Dominik Dellermann, Arthur Hjorth, Dóra Verasztó, Constance Elizabeth Kampf, et al.. Deskilling, Upskilling, and Reskilling: a Case for Hybrid Intelligence. Morals & Machines, 2021, 1 (2), pp.24 - 39. ⟨10.5771/2747-5174-2021-2-24⟩. ⟨hal-04500314⟩
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