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Human-centred AI in industry 5.0: a systematic review

Author

Listed:
  • Mario Passalacqua

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal, UQÀM - Département de Psychologie - UQAM - Université du Québec à Montréal = University of Québec in Montréal)

  • Robert Pellerin

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal)

  • Florian Magnani

    (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon, Institut d'Administration des Entreprises (IAE) - Lyon, UJML - Université Jean Moulin - Lyon 3 - Université de Lyon)

  • Philippe Doyon-Poulin

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal)

  • Laurène Del-Aguila

    (HEC Montréal - HEC Montréal)

  • Jared Boasen

    (HEC Montréal - HEC Montréal)

  • Pierre-Majorique Léger

    (HEC Montréal - HEC Montréal)

Abstract

Industry 4.0 (I4.0) is a manufacturing paradigm revolutionising production by integrating advanced technologies, like AI, for automation and data integration. However, research in I4.0 overlooks human factors, crucial for designing systems that enhance well-being, trust, motivation, and performance. To address this, international bodies have introduced Industry 5.0, aiming to balance technological advancement with human welfare. To transition towards this vision, an understanding of current human-technology interaction is essential. Through a conceptual model aiming to understand the psychological experience of workers within their environment, we identified the studied human factors, their antecedents, consequences, and methodologies. Additionally, we explored how future research can adopt a human-centred approach in designing and implementing technology. Analysis of 67 articles showed the psychosocial dimension of human factors like AI trust, worker autonomy, motivation, and stress are underrepresented. We observed a significant disconnect between empirical and non-empirical studies in terms of theoretical frameworks, variable selection, data collection methods, and research designs. Our findings highlight the necessity for experimental, theory-driven research in human-AI interaction, using a multi-method approach including perceptual, observational, and psychophysiological measures. Lastly, we discuss the integration of these findings into managerial practice to foster workplaces that are technologically advanced yet remain empathetic to human needs.

Suggested Citation

  • Mario Passalacqua & Robert Pellerin & Florian Magnani & Philippe Doyon-Poulin & Laurène Del-Aguila & Jared Boasen & Pierre-Majorique Léger, 2024. "Human-centred AI in industry 5.0: a systematic review," Post-Print hal-04723054, HAL.
  • Handle: RePEc:hal:journl:hal-04723054
    DOI: 10.1080/00207543.2024.2406021
    Note: View the original document on HAL open archive server: https://hal.science/hal-04723054v1
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