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

Alain Lacroux () and Christelle Martin Lacroux ()
Additional contact information
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

Post-Print from HAL

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.

Date: 2026-10-03
References: Add references at CitEc
Citations:

Published in International Journal of Selection and Assessment, 2026, 34 (4), ⟨10.1111/ijsa.70085⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05778155

DOI: 10.1111/ijsa.70085

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().