Participación de los trabajadores y riesgos psicosociales en el contexto de gestión algorítmica e inteligencia artificial

Autores/as

  • Óscar Molina Romo Centre d’Estudis Sociològics sobre la Vida Quotidiana i el Treball (QUIT), Institut d’Estudis del Treball (IET), Universitat Autònoma de Barcelona – Spain
  • Pablo Sanz de Miguel Facultad de Ciencias Sociales y del Trabajo, Departamento de Psicología y Sociología https://orcid.org/0000-0001-6061-8556
  • Juan Arasanz Diaz Notus Applied Social Research https://orcid.org/0000-0002-5778-3566
  • Maria Caprile Elola-Olaso Notus - applied social research

Resumen

Este artículo analiza los sistemas de gestión de trabajadores basados en inteligencia artificial (GTIA) en relación con los riesgos psicosociales de los trabajadores y el papel de las estructuras de representación de los trabajadores en la prevención de estos riesgos. Se basa en la literatura existente para examinar las consecuencias derivadas de la GTIA e investiga cómo los representantes de los trabajadores pueden prevenir y mitigar los resultados no deseados. Sin embargo, la representación de los trabajadores puede enfrentarse a obstáculos a causa de la naturaleza poderosa pero enigmática de la GTIA, así como el equilibrio de poder entre empresarios y empleados por sector, donde se requeriría más investigación para encontrar soluciones.

Palabras clave

Gestión algorítmica, Inteligencia artificial, Sistemas de gestión de trabajadores, Riesgos psicosociales, Representación de los trabajadores, Plataformas digitales, Diálogo social, Negociación colectiva, Democracia industrial

Citas

Aloisi, Antonio & De Stefano, Valerio (2022). Your boss is an algorithm: artificial intelligence, platform work and labour. Bloomsbury Publishing.

Baiocco, Sara; Fernández-Macías, Enrique; Rani, Uma & Pesole, A. (2022). The Algorithmic Management of work and its implications in different contexts (No. 2022/02). JRC Working Papers Series on Labour, Education and Technology. https://www.ilo.org/sites/default/files/wcmsp5/groups/public/%40ed_emp/documents/publication/wcms_849220.pdf

Ball, Kirstie (2021). Electronic Monitoring and Surveillance in the Workplace. Literature review and policy recommendations. Publications Office of the European Union, Luxembourg. https://data.europa.eu/doi/10.2760/451453

Benlian, Alexander; Wiener, Martin; Cram, W. Alec; Krasnova, Hanna; Maedche, Alexander; Möhlmann, Mareike; Recker, Jan & Remus, Ulrich (2022). Algorithmic management: bright and dark sides, practical implications, and research opportunities. Business & Information Systems Engineering, 64(6), 825-839. https://doi.org/10.1007/s12599-022-00764-w

Bérastégui, Pierre (2021). Exposure to psychosocial risk factors in the gig economy: a systematic review. ETUI Research Paper-Report.

Bechter, Barbara; Brandl, Bernd & Lehr, Alex (2022). The role of the capability, opportunity, and motivation of firms for using human resource analytics to monitor employee performance: A multi‐level analysis of the organisational, market, and country context. New Technology, Work and Employment, 37(3), 398-424. https://doi.org/10.1111/ntwe.12239

Bråten, Mona; Andersen, Rolf K.; Flatland, Tord & Tranvik, Tommy (2023). Digitalisation, privacy protection and union rep participation. FAFO. https://www.fafo.no/zoo-publikasjoner/digitalisering-personvern-og-tillitsvalgtes-medvirkning

Cefaliello, Aude (2023). An Occupational Health and Safety Perspective on EU Initiatives to Regulate Platform Work: Patching up Gaps or Structural Game Changers? Journal of Work Health and Safety Regulation, 1(1), 117-137. https://doi.org/10.57523/jaohlev.21-008

Cefaliello, Aude; Moore, Phoebe V. & Donoghue, Robert (2023). Making algorithmic management safe and healthy for workers: addressing psychosocial risks in new legal provisions. European Labour Law Journal, 14(2), 192-210. https://doi.org/10.1177/20319525231167476

Chan, Victor C. H.; Ross, Gwyneth B.; Clouthier, Allison L.; Fischer, Steven L. & Graham, Ryan B. (2022). The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review. Applied Ergonomics, 98. https://doi.org/10.1016/j.apergo.2021.103574

De Stefano, Valerio & Maes, Simon (2023). Algorithmic management and collective bargaining. Transfer: European Review of Labour and Research, 29(1), 21-36. https://doi.org/10.1177/10242589221141055

Doellgast, Virginia; Wagner, Ines & O’Brady, Sean (2023). Negotiating limits on algorithmic management in digitalised services: cases from Germany and Norway. Transfer: European Review of Labour and Research, 29(1), 105-120. https://doi.org/10.1177/1024258922114304

European Agency for Safety and Health at Work [EU-OSHA] (2022a). Artificial intelligence for worker management: an overview. https://osha.europa.eu/en/publications/artificial-intelligence-worker-management-overview

European Agency for Safety and Health at Work [EU-OSHA] (2022b), Artificial intelligence for worker management: implications for occupational safety and health. https://osha.europa.eu/en/publications/artificial-intelligence-worker-management-implications-occupational-safety-and-health

European Agency for Safety and Health at Work [EU-OSHA] (2022c). Third European Survey of Enterprises on New and Emerging Risks (ESENER 2019): Overview Report How European workplaces manage safety and health. https://osha.europa.eu/en/publications/artificial-intelligence-worker-management-implications-occupational-safety-and-health

European Agency for Safety and Health at Work [EU-OSHA] (2024). Worker management through AI - From technology development to the impacts on workers and their safety and health. EU-OSHA.

Eurofound (2016). Mapping key dimensions of industrial relations. Publications Office of the European Union, Luxembourg.

Eurofound (2023). Measuring key dimensions of industrial relations and industrial democracy. Publications Office of the European Union, Luxembourg.

European Company Survey (2019). Data visualization. Eurofound.

Jetha, Arif; Bakhtari, Hela; Rosella, Laura C.; Gignac, Monique A. M.; Biswas, Aviroop; Shahidi, Faraz V.; Smith, Brendan T.; Smith, Maxwell J.; Mustard, Cameron; Khan, Naimul; Arrandale, Victoria H.; Loewen, Peter J.; Zuberi, Daniyal; Dennerlein, Jack T.; Bonaccio, Silvia; Wu, Nicole; Irvin, Emma & Smith, Peter M. (2023). Artificial intelligence and the work–health interface: a research agenda for a technologically transforming world of work. American Journal of Industrial Medicine, 66(10), 815‐830. https://doi.org/10.1002/ajim.23517

Kaufman, Bruce E. (2014). Explaining Breadth and Depth of Employee Voice across Firms: A Voice Factor Demand Model. Journal of Labor Research, 35, 296-319. https://doi.org/10.1007/s12122-014-9185-5

Kellogg, Katherine C.; Valentine, Melissa A. & Christin, Angèle (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174

Krämer, Clara & Cazes, Sandrine (2022). Shaping the transition: Artificial intelligence and social dialogue. OECD Social, Employment and Migration Working Papers No. 279. https://doi.org/10.1787/1815199X

Lee, Min Kyung; Kusbit, Daniel; Metsky, Evan & Dabbish, Laura (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1603-1612). Association for Computing Machinery. https://doi.org/10.1145/2702123.2702548

Meijerink, Jeroen & Bondarouk, Tanya (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review, 33(1), Article 100876. https://doi.org/10.1016/j.hrmr.2021.100876

Molina, Oscar; Butollo, Florian; Makó, Csaba; Godino, Alejandro; Holtgrewe, Ursula; Illsoe, Anna; Junte, Sander; Larsen, Trine Pernille; Illésy, Miklós & Pap, Jószef (2023). It takes two to code: a comparative analysis of collective bargaining and artificial intelligence. Transfer: European Review of Labour and Research, 29(1), 87-104. https://doi.org/10.1177/10242589231156515

Molina, Oscar; Caprile, Maria; Arasanz, Juan & Sanz de Miguel, Pablo (2024). Worker participation and representation: the impact on risk prevention of AI worker management systems. Report. European Agency for Safety and Health at Work (EU-OSHA). http://doi.org/10.2802/7488542

Moore, Phoebe V. (2019). OSH and the future of work: Benefits and risks of artificial intelligence tools in workplaces. En Vincent G. Duffy (Ed.), Digital human modeling and applications in health, safety, ergonomics and risk management. Human body and motion. HCII 2019. Lecture notes in computer science (vol. 11581). Springer. https://doi.org/10.1007/978-3-030-22216-1_22

Mougdir, Sienna (2024). What is in the black box: The ethical implications of algorithms and transparency in the age of the GDPR. Journal of AI, Robotics & Workplace Automation, 3(1), 90-100. http://dx.doi.org/10.69554/TQEW5855

Payá Castiblanque, Raúl & Pizzi, Alejandro (2020). Presencia sindical y gestión de riesgos laborales de origen psicosocial. Un análisis del caso español. Revista Internacional de Organizaciones, 24, 325-366. https://doi.org/10.17345/rio24.325-366

Pereira, Vijay; Hadjielias, Elias; Christofi, Michael & Vrontis, Demetris (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), Article 100857. https://doi.org/10.1016/j.hrmr.2021.100857

Rani, Uma & Furrer, Marianne (2021). Digital labour platforms and new forms of flexible work in developing countries: Algorithmic management of work and workers. Competition & Change, 25(2), 212-236. https://doi.org/10.1177/1024529420905187

Ropponen, Annina; Hakanen, Jari J.; Hasu, Mervi & Seppänen, Laura (2019). Workers’ health, wellbeing, and safety in the digitalizing platform economy. En Seppo Poutanen, Anne Kovalainen & Petri Rouvinen (Eds.), Digital work and the platform economy (pp. 56-73). Routledge.

Shajari, Shaghayegh; Kuruvinashetti, Kirankumar;, Komeili, Amin, & Sundararaj, Uttandaraman (2023). The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review. Sensors, 23(23), 9498. https://doi.org/10.3390/s23239498

Todolí-Signes, Adrián (2021). Making algorithms safe for workers: Occupational risks associated with work managed by artificial intelligence. Transfer: European Review of Labour and Research, 27(4), 433-452.

Underhill, Elsa (2022). The Decline of Trade Unions and Worker Representation. In: Paula Brough, Elliroma Gardiner & Kevin Daniels (Eds.), Handbook on Management and Employment Practices. Handbook Series in Occupational Health Sciences (Vol 3., pp. 855-871). Springer. https://doi.org/10.1007/978-3-030-29010-8_40

Walters, David (2011). Worker representation and psycho-social risks: a problematic relationship? Safety Science, 49(4), 599-606. https://doi.org/10.1016/j.ssci.2010.09.008

Webb, Sidney & Webb, Beatrice (1897). Industrial democracy, new edition in two volumes bound in one. Longmans, Green & Co.

Wood, Alex J. (2021). Algorithmic management: Consequences for work organisation and working conditions. JRC Working Papers Series on Labour, Education and Technology, No. 2021/07. European Commission, Joint Research Centre (JRC). https://hdl.handle.net/10419/233886

Wood, Alex J.; Graham, Mark; Lehdonvirta, Vili & Hjorth, Isis (2019). Good gig, bad gig: autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 33(1), 56-75. https://doi.org/10.1177/0950017018785616

Biografía del autor/a

Óscar Molina Romo, Centre d’Estudis Sociològics sobre la Vida Quotidiana i el Treball (QUIT), Institut d’Estudis del Treball (IET), Universitat Autònoma de Barcelona – Spain

Profesor agregado laboral de la UAB. Sus intereses de investigación giran en torno a las relaciones laborales y los sistemas de negociación colectiva desde una perspectiva comparada, prestando particular atención a la evolución del diálogo social y la concertación. Corresponsal de Eurofound.

Pablo Sanz de Miguel, Facultad de Ciencias Sociales y del Trabajo, Departamento de Psicología y Sociología

Doctor en Sociología por la Universidad Autónoma de Barcelona y Profesor ayudante doctor de la Universidad de Zaragoza. Sus áreas de interés son la gobernanza europea del empleo, las relaciones laborales, las condiciones de empleo y trabajo y las políticas de empleo.

Juan Arasanz Diaz, Notus Applied Social Research

Licenciado en Sociología por la Universidad de Barcelona (2001) y Máster en Políticas Públicas y Sociales (Universidad Pompeu Fabra y John Hopkins University). Sus áreas de interés se centran en el análisis de las condiciones de trabajo, las relaciones laborales y las políticas de empleo y bienestar social.

Maria Caprile Elola-Olaso, Notus - applied social research

Socia fundadora de Notus y directora de investigación. Socióloga por la Universidad de Barcelona con postgrado en ciencias políticas y sociales por la Universidad Pompeu Fabra. Cuenta con más de 20 años de experiencia en asistencia técnica e investigación sobre mercado de trabajo, condiciones de trabajo, políticas de empleo e igualdad de género.

Publicado

2024-12-20

Cómo citar

Molina Romo, Óscar, Sanz de Miguel, P. ., Arasanz Diaz, J., & Caprile Elola-Olaso, M. . (2024). Participación de los trabajadores y riesgos psicosociales en el contexto de gestión algorítmica e inteligencia artificial. Anuario IET De Trabajo Y Relaciones Laborales, 10, e125. https://doi.org/10.5565/rev/aiet.125

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