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Governing the work-related risks of AI: implications for the German government and trade unions

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  • Anke Hassel

    (Hertie School, Berlin’s University of Governance, Germany)

  • Didem Özkiziltan

    (University of Parma, Italy)

Abstract

This article discusses the risks that artificial intelligence (AI) poses for work. It classifies risks into two types, direct and indirect. Direct risks are AI-induced forms of discrimination, surveillance and information asymmetries at work. Indirect risks are enhanced workplace automation and the increasing ‘fissurisation’ of work. Direct and indirect risks are illustrated using the example of the transport and logistics sector. We discuss policy responses to both types of risk in the context of the German economy and argue that the policy solutions need to differ according to the type of risk. Direct risks can be addressed by European and national regulation against discrimination, surveillance and information asymmetries. As for indirect risks, the first step is to monitor the risks so as to gain an understanding of sector-specific transformations and establish relevant expertise and competence. This way of addressing AI-induced risks at work will help to improve the prospects of decent work, fair remuneration and adequate social protection for all.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:treure:v:29:y:2023:i:1:p:71-86
    DOI: 10.1177/10242589221147228
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    References listed on IDEAS

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    1. Anke Hassel & Felix Sieker, 2022. "The platform effect: How Amazon changed work in logistics in Germany, the United States and the United Kingdom," European Journal of Industrial Relations, , vol. 28(3), pages 363-382, September.
    2. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    3. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    4. 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.
    5. 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.
    6. Ljubica Nedelkoska & Glenda Quintini, 2018. "Automation, skills use and training," OECD Social, Employment and Migration Working Papers 202, OECD Publishing.
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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. Didem Özkiziltan, 2024. "Governing Engels’ Pause: AI and the World of Work in Germany," ILR Review, Cornell University, ILR School, vol. 77(5), pages 846-856, October.

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