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From risk mitigation to employee action along the machine learning pipeline: A paradigm shift in European regulatory perspectives on automated decision-making systems in the workplace

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  • Mollen, Anne
  • Hondrich, Lukas

Abstract

Automated decision-making (ADM) systems in the workplace aggravate the power imbalance between employees and employers by making potentially crucial decisions about employees. Current approaches focus on risk mitigation to safeguard employee interests. While limiting risks remains important, employee representatives should be able to include their interests in the decision-making of ADM systems. This paper introduces the concept of the Machine Learning Pipeline to demonstrate how these interests can be implemented in practice and point to necessary structural transformations.

Suggested Citation

  • Mollen, Anne & Hondrich, Lukas, 2023. "From risk mitigation to employee action along the machine learning pipeline: A paradigm shift in European regulatory perspectives on automated decision-making systems in the workplace," Working Paper Forschungsförderung 278, Hans-Böckler-Stiftung, Düsseldorf.
    • Handle: RePEc:zbw:hbsfof:278
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    References listed on IDEAS

    as
    1. Will Dobbie & Jacob Goldin & Crystal S. Yang, 2018. "The Effects of Pretrial Detention on Conviction, Future Crime, and Employment: Evidence from Randomly Assigned Judges," American Economic Review, American Economic Association, vol. 108(2), pages 201-240, February.
    2. Krzywdzinski, Martin & Pfeiffer, Sabine & Evers, Maren & Gerber, Christine, 2022. "Measuring work and workers: Wearables and digital assistance systems in manufacturing and logistics," Discussion Papers, Research Group Globalization, Work, and Production SP III 2022-301, WZB Berlin Social Science Center.
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    Keywords

    Artificial Intelligence; EU regulation; workplace; democracy; employee representatives;
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