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It takes two to code: a comparative analysis of collective bargaining and artificial intelligence

Author

Listed:
  • Oscar Molina

    (Universitat Autònoma de Barcelona (QUIT), Spain)

  • Florian Butollo

    (Berlin Social Science Centre (WZB), Germany)

  • Csaba Makó

    (Institute for Sociology, Hungarian Academy of Social Sciences (TKSZI), Hungary)

  • Alejandro Godino

    (Universitat Autònoma de Barcelona (QUIT), Spain)

  • Ursula Holtgrewe

    (Zentrum für soziale Innovation (ZSI), Germany)

  • Anna Illsoe

    (University of Copenhagen (FAOS), Denmark)

  • Sander Junte

    (Universitat Autònoma de Barcelona (QUIT), Spain)

  • Trine Pernille Larsen

    (University of Copenhagen (FAOS), Denmark)

  • Miklós Illésy

    (Institute for Sociology, Hungarian Academy of Social Sciences (TKSZI), Hungary)

  • Jószef Pap

    (Institute for Sociology, Hungarian Academy of Social Sciences (TKSZI), Hungary)

  • Philip Wotschack

    (Berlin Social Science Centre (WZB), Germany)

Abstract

The extension of artificial intelligence (AI) and algorithmic management mechanisms by companies has led to growing trade union demands to regulate their use. This article explores the role of collective bargaining and employee participation mechanisms in regulating the use by companies of AI and algorithms. This is done through a comparative analysis of institutional developments at EU level, as well as in four countries with different industrial relations models (Denmark, Germany, Hungary and Spain). The article shows that there are remarkable differences between countries in the roles of social partners and in the combination of protective and participative mechanisms used to respond to the challenges of AI and algorithmic management. However, the analysis also serves to highlight the limits of existing institutions and practices to cope with the complexity of challenges associated with AI and algorithmic management. This calls for institutional adaptation and additional regulatory efforts at EU and national levels to support collective bargaining.

Suggested Citation

  • Oscar Molina & Florian Butollo & Csaba Makó & Alejandro Godino & Ursula Holtgrewe & Anna Illsoe & Sander Junte & Trine Pernille Larsen & Miklós Illésy & Jószef Pap & Philip Wotschack, 2023. "It takes two to code: a comparative analysis of collective bargaining and artificial intelligence," Transfer: European Review of Labour and Research, , vol. 29(1), pages 87-104, February.
    • Handle: RePEc:sae:treure:v:29:y:2023:i:1:p:87-104
    DOI: 10.1177/10242589231156515
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    References listed on IDEAS

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    1. Daniel R. A. Schallmo & Christopher A. Williams, 2018. "Digital Transformation Now!," SpringerBriefs in Business, Springer, number 978-3-319-72844-5, October.
    2. Gerhard BOSCH, 2015. "Shrinking collective bargaining coverage, increasing income inequality: A comparison of five EU countries," International Labour Review, International Labour Organization, vol. 154(1), pages 57-66, March.
    3. Marguerita Lane & Anne Saint-Martin, 2021. "The impact of Artificial Intelligence on the labour market: What do we know so far?," OECD Social, Employment and Migration Working Papers 256, OECD Publishing.
    4. K. Sabeel Rahman & Kathleen Thelen, 2019. "The Rise of the Platform Business Model and the Transformation of Twenty-First-Century Capitalism," Politics & Society, , vol. 47(2), pages 177-204, June.
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    Cited by:

    1. Valerio De Stefano & Virginia Doellgast, 2023. "Introduction to the Transfer special issue. Regulating AI at work: labour relations, automation, and algorithmic management," Transfer: European Review of Labour and Research, , vol. 29(1), pages 9-20, February.
    2. Anke Hassel & Didem Özkiziltan, 2023. "Governing the work-related risks of AI: implications for the German government and trade unions," Transfer: European Review of Labour and Research, , vol. 29(1), pages 71-86, February.

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