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

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  • 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
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