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arXiv AI

From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance

The paper reviews the evolution of AI recruitment systems from simple profile matching to complex, multi‑stage workflows that retrieve evidence, compare candidates, and execute actions. It analyzes 40 representative works, highlighting transitions from similarity to reciprocal suitability, from single models to compound workflows, and from offline predictions to evidence‑aligned evaluation. The authors identify persistent gaps—such as confounded behavioral labels, limited data validity, and lack of privacy assessment—and propose a staged mapping for defensible evaluation and an agenda for auditable, evidence‑grounded systems.

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From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance

The paper reviews the evolution of AI recruitment systems from simple profile matching to complex, multi‑stage workflows that retrieve evidence, compare candidates, and execute actions. It organizes 40 representative works, highlighting transitions from similarity to reciprocal suitability, from single models to compound workflows, and from offline prediction to evidence‑aligned evaluation. The authors identify persistent gaps such as confounded behavioral labels, limited data validity, hidden pipeline failures, and a lack of privacy assessment, and propose a staged mapping for defensible evaluation and an agenda for evidence‑grounded, auditable systems.

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