International Journal of Selection and Assessment· 2026Q1
When Recruiters Meet Algorithms: Advice Taking and Bias in AI‐Assisted Recruitment
- 0citations
- Q1SCImago
- 2026year
Short summary
Recruiters are behaviorally more influenced by algorithmic advice (ARS) than human expert advice, despite reporting lower trust in the ARS, a trust-reliance paradox.
AI-generated from the title and abstract; the full text is not read.
Key points
- Recruiters are behaviorally more influenced by algorithmic advice than human expert advice, despite reporting lower trust in the algorithm.
- Convergent advice, regardless of source, was the most influential in shaping recruiters' decisions.
- Inaccurate advice negatively impacted hiring decision quality more than accurate advice improved it.
- 1,208 recruitment professionals participated in a between-subjects experiment manipulating advice source, accuracy, and number of advisors.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. International Journal of Selection and Assessment, 2026 · DOI ↗
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