Impacte de la gestió algorítmica dels recursos humans en les condicions de treball:el rol de la negociació col·lectiva

  1. Raúl Payá Castiblanque 1
  1. 1 Universitat de València
    info
    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

    Geographic location of the organization Universitat de València
Journal:
IDP: revista de Internet, derecho y política = revista d'Internet, dret i política

ISSN: 1699-8154

Year of publication: 2025

Issue: 43

Type: Article

DOI: 10.7238/IDP.V0I43.432879 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: IDP: revista de Internet, derecho y política = revista d'Internet, dret i política

Abstract

This article analyses the impact of algorithmic human resource management on working conditions and their regulation through collective bargaining. Statistical analysis based on a Eurobarometer of the European Agency for Safety and Health at Work shows that those working under algorithmic manage-ment experience greater work intensification, time pressure and constant surveillance, which increases the likelihood of stress and other psychosomatic pathologies. From a regulatory perspective, the study examines the collective bargaining response to these challenges. It finds that, despite the proposals of the Fifth Agreement on Employment and Collective Bargaining and the European Framework Agree-ment on Digitalisation, the incorporation of specific clauses in collective agreements remains insuffi-cient. However, collective bargaining and algorithmic co-management are crucial to ensuring effective protection against the negative effects of algorithmic human resource management.

Bibliographic References

  • AGOTE, R.; ARRIOLA, A. (2024). «Claves del régimen jurídico de los sistemas de inteligencia artificial para la gestión de las relaciones laborales en España». Iuslabor, n.º 3, págs. 149-187.
  • ALOISI, A.; GRAMANO, E. (2019). «Artificial intelligence is watching you at work: Digital surveillance, employee monitoring, and regulatory issues in the EU context». Comparative Labor Law & Policy Journal, vol. 41, n.º 1, págs. 95-122.
  • ALOISI, A.; POTOCKA-SIONEK, N. (2022). «De-gigging the labour market? An analysis of the ‘algorithmic management’ provisions in the proposed Platform Work Directive». Italian Labour Law E-Journal, vol.15, n.º 1, págs. 29-50. DOI: https://doi.org/10.6092/issn.1561-8048/15027
  • ARANA-LANDÍN, G.; LASKURAIN-ITURBE, I.; ITURRATE, M., et al. (2023). «Assessing the influence of industry 4.0 technologies on occupational health and safety». Heliyon, vol. 9, n.º 3, págs. e13720. DOI: https://doi.org/10.1016/j.heliyon.2023.e13720
  • CAMAS RODA, F. (2024). Country report non-discrimination. Transposition and implementation at national level of Council Directives 2000/43 and 2000/78: Spain 2024. Luxemburgo: Publications Office of the European Union.
  • CEFALIELLO, A.; MOORE, P. V.; DONOGHUE, R. (2023). «Making algorithmic management safe and healthy for workers: Addressing psychosocial risks in new legal provisions». European Labour Law Journal, vol. 14, n.º 2, págs. 192-210. DOI: https://doi.org/10.1177/20319525231167476
  • CHRISTENKO, A.; JANKAUSKAIT, V.; PALIOKAIT, A., et al. (2022). Artificial Intelligence for Worker Management: An Overview. Luxemburgo: European Agency for Safety and Health at Work.
  • COLLINS, P.; ATKINSON, J. (2023). «Worker voice and algorithmic management in post-Brexit Britain». Transfer: European Review of Labour and Research, vol. 29, n.º 1, págs. 37-52. DOI: https://doi. org/10.1177/10242589221143068
  • DE STEFANO, V. (2020). «‘Masters and servers’: Collective labour rights and private government in the contemporary world of work». International Journal of Comparative Labour Law and Industrial Relations, vol. 36, n.º 4, págs. 425-444. DOI: http://dx.doi.org/10.54648/IJCL2020022
  • DE STEFANO, V.; TAES, S. (2021). Algorithmic Management and Collective Bargaining. Bruselas: ETUI.
  • DE STEFANO, V.; TAES, S. (2023). «Algorithmic management and collective bargaining». Transfer: European Review of Labour and Research, vol. 29, n.º 1, págs. 21-36. DOI: https://doi. org/10.1177/10242589221141055
  • DUGGAN, J.; SHERMAN, U.; CARBERY, R., et al. (2020). «Algorithmic management and app‐work in the gig economy: A research agenda for employment relations and HRM». Human Resource Management Journal, vol. 30, n.º 1, págs. 114-132. DOI: https://doi.org/10.1111/1748-8583.12258
  • ELLIOTT, D.; SOIFER, E. (2022). «AI Technologies, Privacy, and Security». Frontiers in Artificial Intelligence, vol. 5. págs. 826737. DOI: https://doi.org/10.3389/frai.2022.826737
  • GILBERT, A.; THOMAS, A.; PISSARIDES, C., et al. (2021). The Amazonian Era: How Algorithmic Systems are Eroding Good Work. Londres: Institute for the Future of Work.
  • HOWARD, J. (2022). «Algorithms and the future of work». American Journal of Industrial Medicine, vol. 65, n.º 12, págs. 943-952. DOI: https://doi.org/10.1002/ajim.23429
  • JETHA, A.; BAKHTARI, H.; ROSELLA, L. C., et al. (2023). «Artificial intelligence and the work–health interface: research agenda for a technologically transforming world of work». American Journal of Industrial Medicine, vol. 66, n.º 10, págs. 815‐830. DOI: https://doi.org/10.1002/ajim.23517
  • KALISCHKO,T.; RIEDL, R. (2021). «Electronic Performance Monitoring in the Digital Workplace: Conceptualization, Review of Effects and Moderators, and Future Research Opportunities». Frontiers in Psychology, vol. 12, págs. 633031. DOI: https://doi.org/10.3389/fpsyg.2021.633031
  • KIM, P.; BODIE, M. T. (2021). Artificial Intelligence and the Challenges of Workplace Discrimination and Privacy. 35 ABA Journal of Labor and Employment Law 289. Washington University: Louis Legal Studies Research Paper.
  • KUNST, D. (2020). Deskilling among Manufacturing Production Workers. Amsterdam: Tinbergen Institute Discussion Papers. DOI: https://doi.org/10.2139/ssrn.3429711
  • LANE, M.; SAINT-MARTIN, A. (2021). The impact of artificial intelligence on the labour market: What do we know so far? OECD Social, Employment and Migration Working Papers, n.º 256. París: OECD.
  • MAUNO, S.; HERTTALAMPI, M.; MINKKINEN, J., et al. (2022). «Is work intensification bad for employees? A review of outcomes for employees over two decades». Work & Stress, vol. 37, n.º 1, págs. 100-125. DOI: https://doi.org/10.1080/02678373.2022.2080778
  • MOLINA, O.; BUTOLLO, F.; MAKÓ, C., et al. (2023). «It takes two to code: a comparative analysis of collective bargaining and artificial intelligence». Transfer: European Review of Labour and Research, vol. 29, n.º 1, págs. 87-104. DOI: https://doi.org/10.1177/10242589231156515
  • NEWLANDS, G. (2021). «Algorithmic Surveillance in the Gig Economy: The Organization of Work through Lefebvrian Conceived Space». Organization Studies, vol. 42, n.º 5, págs. 719-737. DOI: https://doi. org/10.1177/0170840620937900
  • PAYÁ, R.; PIZZI, A. (2024). «Relación entre determinados usos de la inteligencia artificial y los riesgos psicosociales en entornos laborales europeos». Archivos de Prevención de Riesgos Laborales, vol. 27, n.º 3, págs. 233-249. DOI: https://doi.org/10.12961/aprl.2024.27.03.02
  • PEÑA MONCHO, J. (2025). Los derechos de información y consulta frente a la dirección algorítmica del trabajo. Propuestas para impulsar la participación de las personas trabajadoras ante la IA. Pamplona: Aranzadi.
  • RODRÍGUEZ-FERNÁNDEZ, M. L. (2024). «Negociación colectiva de algoritmos y sistemas de inteligencia artificial (IA) para acotar el poder de dirección de la empresa. La experiencia de España». Presente y futuro de la libertad sindical, págs. 355-371. València: Tirant Lo Blanch.
  • TODOLÍ-SIGNES, A. (2024). «La directiva de trabajo en plataformas digitales. Puntos conflictivos y recomendaciones para la trasposición». Temas laborales: Revista andaluza de trabajo y bienestar social, vol. 173, págs. 45-73.
  • TRADES UNION CONGRESS (2021). Dignity at Work and the AI Revolution. Londres: Trades Union Congress.
  • USSELL, S.J.; NORVIG, P. (2010). Artificial Intelligence: A Modern Approach. 3a. ed. Nueva Jersey: Pearson Education, Inc.
  • SCHALLMO, D.; WILLIAMS, C. A. (2018). «Digital Transformation of Business Models». Digital Transformation Now!, págs. 9-13. Cham: Springer Briefs in Business. DOI: https://doi.org/10.1007/978-3-319- 72844-5_3
  • UNRUH, C. F.; HAID, C.; JOHANNES, F., et al. (2022). «Human Autonomy in Algorithmic Management». Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES ‘22), págs. 753-762. Oxford: Association for Computing Machinery. DOI: https://doi.org/10.1145/3514094.3534168
  • VALLOR, S. (2015). «Moral Deskilling and Upskilling in a New Machine Age: Reflections on the Ambiguous Future of Character». Philosophy & Technology, vol. 28, págs. 107-124. DOI: https://doi. org/10.1007/s13347-014-0156-9
  • WEST, D. M. (2021). «How employers use technology to surveil employees». Brookings [en línea]. Disponible en: https://www.brookings.edu/articles/how-employers-use-technology-to-surveil-employees/. [Fecha de consulta: 28 de febrero de 2025].
  • WOOD, A.J.; GRAHAM, M.; LEHDONVIRTA, V., et al. (2019). «Good gig, bad gig: Autonomy and algorithmic control in the global gig economy». Work, Employment and Society, vol. 33, n.º 1, págs. 56-75. DOI: https://doi.org/10.1177/0950017018785616
  • ZORZENON, R.; LIZARELLI, F. L.; DE A. MOURA, D. B. A. (2022). «What is the potential impact of industry 4.0 on health and safety at work?». Safety Science, vol. 153, pág, 105802. DOI: https://doi. org/10.1016/j.ssci.2022.105802
Data source: Dialnet