Human-AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring

Bernd Irlenbusch (), Holger Rau () and Rainer Rilke ()
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Bernd Irlenbusch: University of Cologne
Holger Rau: University of Duisburg-Essen and University of Göttingen
Rainer Rilke: Economics Group, WHU - Otto Beisheim School of Management

No 18517, IZA Discussion Papers from IZA Network @ LISER

Abstract: We study how human versus LLM-based evaluation and gender transparency shape entry into competitive jobs. In a preregistered online experiment, participants first complete a Niederle and Vesterlund (2007) tournament task to measure competitive preferences, then prepare text-based job applications and decide whether to apply under each of four evaluation regimes—human only, LLM only, and two hybrid human-in-the-loop configurations—while gender disclosure is randomized between subjects. LLM involvement reduces application rates, with stronger effects for women than men, including under hybrid designs. Effects are driven by non-competitive candidates; non-competitive women, the group most exposed to AI-induced deterrence, receive the strongest objective evaluations under pure AI assessment across all subgroups, yet are systematically underconfident and apply least often. Competitive men persistently apply and exhibit overconfidence-driven adverse selection, whereas competitive women show resilience to AI-induced deterrence while remaining well-calibrated under AI evaluation and exhibiting positive self-selection across regimes. We find no effects of gender transparency.

Keywords: AI hiring; LLMs; algorithm aversion; gender differences (search for similar items in EconPapers)
JEL-codes: C92 J24 J71 O33 (search for similar items in EconPapers)
Date: 2026-04
New Economics Papers: this item is included in nep-ain, nep-eur, nep-exp, nep-gen, nep-hrm, nep-lma and nep-mid
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