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

Bernd Irlenbusch (), Holger A. Rau () and Rainer Michael Rilke ()
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Bernd Irlenbusch: University of Cologne & London School of Economics and Political Science
Holger A. Rau: University of Duisburg-Essen & University of Gottingen
Rainer Michael Rilke: WHU – Otto Beisheim School of Management

No 398, ECONtribute Discussion Papers Series from University of Bonn and University of Cologne, Germany

Abstract: LLMs are rapidly entering the hiring process, but their most pronounced effects may occur before any screening by changing who chooses to apply. 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)
Pages: 57 pages
Date: 2026-03
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