Human-centred AI in industry 5.0: a systematic review

Mario Passalacqua, Robert Pellerin, Florian Magnani (), Philippe Doyon-Poulin, Laurène Del-Aguila, Jared Boasen and Pierre-Majorique Léger
Additional contact information
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: MAGELLAN - Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - 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

Post-Print from HAL

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.

Keywords: Human-centred AI; industry 5.0; industry 4.0; psychosocial factors; human factors (search for similar items in EconPapers)
Date: 2024-10-02
Note: View the original document on HAL open archive server: https://hal.science/hal-04723054v1
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in International Journal of Production Research, 2024, pp.1-32. ⟨10.1080/00207543.2024.2406021⟩

Downloads: (external link)
https://hal.science/hal-04723054v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04723054

DOI: 10.1080/00207543.2024.2406021

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().