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Research questionHow should multi-stage AI recruitment workflows be evaluated so their evidence supports defensible hiring decisions?Recruitment automation now combines retrieval, ranking, assessment, interviewing, sourcing, and human handoff rather than producing only a match score. Final-output metrics can conceal failures within the workflow, while behavioral labels and limited data complicate interpretation and external validity.
AI
AI Agents
Business
Evaluation & Benchmarks
Information Retrieval
Machine Learning
Natural Language Processing
Research Paper
Latest papersRecent research connected to this question, newest first.From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and GovernanceApplies to AI recruitment systems with document understanding, retrieval, ranking, assessment, interviewing, sourcing, LLM components, recruiting agents, and human handoff. The evidence is a purposive systematized narrative review of 40 representative works with supporting industrial and legal sources, not a prevalence estimate. In the coded set, privacy was not directly evaluated, no work jointly evaluated utility, fairness, privacy, and security, and private or synthetic data limited external validity.research paper · Sep 3, 2026
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