AI招聘从匹配模型演进为能执行任务的智能代理，推动全流程自动化。
From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance
- 从简单匹配转向多阶段智能代理，支持证据检索与决策执行。
- 识别出行为标签混淆、数据隐私不足等六大核心挑战。
- 适合关注AI招聘系统评估与治理的研究者与从业者。
人工智能在招聘领域的应用已从对简历配对和排名列表的自动化，发展为支持多阶段工作流的系统，包括证据检索、候选人比较及行动支持或执行。本文通过有目的的文献搜索与编码（截至2026年7月23日，更新至9月2日），梳理了40篇代表性研究，并结合工业与法律资料进行系统性综述。分析显示三个关键转变：从相似性匹配转向互适性评估，从单一模型转向复合工作流，从离线预测转向基于证据与生产率的评价。在文档理解、检索、排序、评估、面试、寻源与人工交接等环节，区分了字段、成对、列表、案例、轨迹和结果级证据。持续存在的缺口包括行为标签混淆曝光、偏好与资质；私有与合成数据削弱外部有效性；最终得分掩盖流水线失败；且所编码研究中无一项同时评估效用、公平性、隐私与安全。因此提出从评估证据到可辩护结论的分阶段映射框架，并倡导可互惠、基于证据、时间可控、选择性与可审计的系统建设。进展应以工作流能否获取正确证据、保留不确定性、支持可争议决策、在明确成本与风险下改善结果来衡量。
原文摘要 · Abstract (English)
Artificial intelligence in recruitment has shifted the object being automated from profile pairs and ranked lists to multi-stage workflows that retrieve evidence, compare candidates, and support or execute actions. This systematized narrative review traces that development from bilateral retrieval and behavioral ranking through neural person--job matching, large language model (LLM) components, and tool-using recruiting agents. Using a purposive search and coding protocol updated through 23 July 2026, plus targeted updates through 2 September 2026, we organize 40 representative works with supporting industrial and legal sources. This synthesis is not a prevalence estimate. We analyze three coupled transitions: from similarity to reciprocal suitability, from a model to a compound workflow, and from offline prediction to evidence- and productivity-aligned evaluation. Across document understanding, retrieval, ranking, assessment, interviewing, sourcing, and human handoff, we distinguish field-, pair-, list-, case-, trajectory-, and outcome-level evidence. Persistent gaps arise because behavioral labels confound exposure, preference, and qualification; private and synthetic data limit external validity; final-output scores conceal pipeline failures; and, within the coded set, privacy is not directly evaluated and no row jointly evaluates utility, fairness, privacy, and security. These observations describe the coded set rather than the field as a whole. We therefore introduce a staged mapping from evaluation evidence to the strongest defensible claim, together with an agenda for reciprocal, evidence-grounded, temporally controlled, selective, and auditable systems. Progress should be judged by whether workflows retrieve the right evidence, preserve uncertainty, support contestable decisions, and improve outcomes under explicit cost and risk constraints.
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