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When Recruiters Meet Algorithms: Advice Taking and Bias in AI‐Assisted Recruitment

Author

Listed:
  • Alain Lacroux

    (UP1 EMS - Université Paris 1 Panthéon-Sorbonne - École de Management de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Christelle Martin Lacroux

    (CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes)

Abstract

ABSTRACT This study examined whether professional recruiters revise their hiring decisions after receiving external advice under cognitive conflict, that is, when advice contradicts their initial evaluation. In a between‐subjects experiment, 1,208 recruitment professionals evaluated two candidates, made an initial hiring decision, and then received advice whose source (human expert vs. algorithmic recruitment system, ARS), accuracy (accurate vs. inaccurate), and number of advisors (one vs. two) were manipulated across eight conditions. Results revealed widespread egocentric advice discounting. Although trust predicted advice utilization, a trust–reliance paradox emerged: recruiters reported lower trust in the ARS yet were behaviorally more influenced by it. Convergent advice was most influential, and exploratory analyses suggested that inaccurate advice degraded decision quality more than accurate advice improved it. Findings underscore the need for calibrated reliance in AI‐assisted selection.

Suggested Citation

  • Alain Lacroux & Christelle Martin Lacroux, 2026. "When Recruiters Meet Algorithms: Advice Taking and Bias in AI‐Assisted Recruitment," Post-Print hal-05778155, HAL.
    • Handle: RePEc:hal:journl:hal-05778155
    DOI: 10.1111/ijsa.70085
    as

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