Better Together: Quantifying the Benefits of AI-Assisted Recruitment
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Computer Science > Computation and Language
Title:Better Together: Quantifying the Benefits of AI-Assisted Recruitment
Abstract:Hiring algorithms have mostly scored the materials recruiters already see. Large language models (LLMs) can instead generate new information about candidates by conducting, at scale, structured interviews once reserved for a few finalists. We study this shift in two field experiments at a recruitment platform. The first experiment holds the candidate pool fixed and randomizes whether recruiters observe the AI Interview Report; the second embeds the AI interview as a requirement in a live hiring pipeline. In both, candidates shortlisted with AI interview information pass the final human interview (conducted blind to shortlisting condition) at rates 17.5 (SE 8.5) to 20 (SE 11.8) percentage points higher than candidates shortlisted from resumes alone. The gains concentrate where resumes are least informative: adding AI Interview Report ratings to conventional candidate features raises out-of-sample AUC by 0.18 for junior candidates, against 0.08 for non-junior candidates. The participation cost falls on applicants as 75 percent of invited candidates do not complete the interview. However, the attrition is itself a signal: completion is more consistent with job-search motivation than with predicted interview performance. AI interviews thus add information exactly where conventional signals fail, and they move the cost of screening from firms to applicants.
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY) |
| Cite as: | arXiv:2507.08029 [cs.CL] |
| (or arXiv:2507.08029v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2507.08029
arXiv-issued DOI via DataCite
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Submission history
From: Emil Palikot [view email][v1] Tue, 8 Jul 2025 22:51:46 UTC (8,227 KB)
[v2] Fri, 7 Aug 2026 15:03:52 UTC (1,375 KB)
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