Artificial intelligence in action: seeing as a machine

L’intelligence artificielle en action: voir comme une machine

Philippe Jean-Baptiste ()
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Philippe Jean-Baptiste: LEST - Laboratoire d'Economie et de Sociologie du Travail - AMU - Aix Marseille Université - CNRS - Centre National de la Recherche Scientifique

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Abstract: PreviewThe article explores the rise of image recognition as an emblematic area of applied artificial intelligence. This technology, based on deep learning and massive analysis of visual data, is transforming many sectors: industry (quality control), health (medical diagnosis), security (monitoring), trade (personalization) or human resources (behavioral assessment).We show here that while these uses make it possible to automate formerly human tasks, they are nevertheless based on unincarnated intelligence: algorithms "see" without understanding, detect without feeling, classify without awareness of the context.This observation raises major ethical, organizational and managerial issues, especially in terms of responsibility, algorithmic biases and surveillance at work. Managers are thus called upon to play a pivotal role in the rational integration of these tools, ensuring both their operational efficiency and their social acceptability. The article is part of a thematic series designed to illuminate the different areas of application of AI, highlighting each time the need for enlightened human management in the face of powerful but meaningless systems. ContentArtificial intelligence in action - see as a machine https://management-datascience.org/articles/55033/

Keywords: technology appropriation; Management; Generative Artificial Intelligence; IAG; Generative AI; future of work; AI and management skills; AI skills; Artificial Intelligence; Social acceptability; AI; Human-AI Collaboration; technology adoption; Intelligence artificielle générative; IA Générative; IA; compétences IA et management; compétences IA; Collaboration Homme-IA; appropriation des technologies; adoption des technologies; acceptabilité sociale (search for similar items in EconPapers)
Date: 2025-08-28
Note: View the original document on HAL open archive server: https://hal.science/hal-05229240v1
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Published in Management & Data Science, 2025, 9 (4)

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