IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v192y2025ics0148296325001213.html
   My bibliography  Save this article

Opening the ‘black box’ of HRM algorithmic biases – How hiring practices induce discrimination on freelancing platforms

Author

Listed:
  • Trautwein, Yannik
  • Zechiel, Felix
  • Coussement, Kristof
  • Meire, Matthijs
  • Büttgen, Marion

Abstract

Online freelancing platforms extensively apply algorithms and AI, for example, to rank freelancers. These platforms are often considered neutral for not displaying freelancers’ gender, race, and age, but recent studies have revealed mounting freelancer complaints of unfair treatment and discrimination stemming from the platforms’ algorithms. Drawing from social dominance theory, this study contributes to the algorithmic HRM literature by uncovering an indirect algorithmic discrimination mechanism explaining bias in algorithmic rankings. By using an Upwork dataset of 44,167 freelancers and leveraging structural equation modeling, we find that the number of jobs completed through the platform mediates the effects of gender, race, and age on the platform’s ranking, demonstrating discrimination against female, Black women, Asian, and younger candidates. The study’s theoretical contributions to the algorithmic HRM literature, the methodological contribution of a novel AI picture analysis tool, and managerial implications for online freelancing platforms and HR departments are discussed.

Suggested Citation

  • Trautwein, Yannik & Zechiel, Felix & Coussement, Kristof & Meire, Matthijs & Büttgen, Marion, 2025. "Opening the ‘black box’ of HRM algorithmic biases – How hiring practices induce discrimination on freelancing platforms," Journal of Business Research, Elsevier, vol. 192(C).
  • Handle: RePEc:eee:jbrese:v:192:y:2025:i:c:s0148296325001213
    DOI: 10.1016/j.jbusres.2025.115298
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296325001213
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2025.115298?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:192:y:2025:i:c:s0148296325001213. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.