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Cited by 46Ethics and Social Impacts of AINames, Identity, and Discrimination ResearchAI and HR Technologies

OK computer: Worker perceptions of algorithmic recruitment

Open access
Fumagalli, Elena, Rezaei, Sarah, Salomons, Anna·2022·Research Policy
DOI 10.1016/j.respol.2021.104420
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Abstract

We provide evidence on how workers on an online platform perceive algorithmic versus human recruitment through two incentivized experiments designed to elicit willingness to pay for human or algorithmic evaluation. In particular, we test how information on workers’ performance affects their recruiter choice and whether the algorithmic recruiter is perceived as more or less gender-biased than the human one. We find that workers do perceive human and algorithmic evaluation differently, even though both recruiters are given the same inputs in our controlled setting. Specifically, human recruiters are perceived to be more error-prone evaluators and place more weight on personal characteristics, whereas algorithmic recruiters are seen as placing more weight on task performance. Consistent with these perceptions, workers with good task performance relative to others prefer algorithmic evaluation, whereas those with lower task performance prefer human evaluation. We also find suggestive evidence that perceived differences in gender bias drive preferences for human versus algorithmic recruitment.

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  • Participants considered that discussing the decision with humans would potentially lead to a change in outcome for cases that were close to the decision boundary.

    “Our participants considered that discussing the decision with humans would potentially lead to a change in outcome for cases that were close to the decision boundary (e.g., Q.27, Q.28; in line with earlier work [36,68])”

    From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [36]

    The Recruiter preferences section reports that workers chose the recruiter they expected to rate them more favorably, supporting the idea that they anticipated different outcomes from human and algorithmic evaluation. The paper does not establish that discussing a decision with a human could change it, or that this expectation concerned cases close to a decision boundary. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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2 of the 51 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. Cowgill, B., Tucker, C., 2020. Algorithmic fairness and economics. Working Paper. https://ssrn.com/abstract=3361280.

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Related

Authors
Fumagalli, E., Rezaei, S., & Salomons, A.
Affiliations
  • Utrecht University (NL)
  • Universität Innsbruck (AT)
Corresponding author
Rezaei, Sarah
ORCID
Venue
Research Policy
Publisher
Elsevier BV
Published
2022-03
Volume
51
Issue
2
Pages
104420
ISSN
0048-7333 (ISSN-L)
DOI
10.1016/j.respol.2021.104420
OpenAlex
W3215242739
Cited by
46
References
51
Citations by year
2026: 7 · 2025: 13 · 2024: 14 · 2023: 11 · 2020: 1
FWCI
7.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Research Policy (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Digital Library of the University of Innsbruck (University of Innsbruck) (submittedVersion, cc-by)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
Funders
  • Universiteit Utrecht
  • Instituut Gak
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
Keywords
employee perceptions, willingness to pay, digital platforms, gender bias, performance evaluation (human resources), task performance, personal characteristics, algorithmic fairness

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