用AI面试报告提升招聘效率，尤其在简历信息不足时效果更明显。
Better Together: Quantifying the Benefits of AI-Assisted Recruitment
- 用大模型生成结构化面试报告，补充传统简历信息
- 有AI报告的候选人通过终面率高出17.5至20个百分点
- 适合简历质量差的初级岗位招聘，筛选成本转嫁给申请者
招聘算法通常仅评估招聘者已有的材料。大语言模型（LLMs）可大规模生成关于候选人的新信息，通过结构化面试取代以往仅限少数入围者的环节。我们在一个招聘平台上开展两项实地实验：第一项固定候选人池，随机分配是否让招聘者查看AI面试报告；第二项将AI面试作为真实招聘流程中的必经环节。结果显示，使用AI面试报告筛选的候选人，在盲评终面中通过率比仅凭简历筛选高出17.5（标准误8.5）至20（标准误11.8）个百分点。该增益集中在简历信息最有限的情况：对初级候选人，加入AI面试评分使样本外AUC提升0.18，而对非初级候选人仅提升0.08。75%受邀参加AI面试的候选人未完成，但完成行为本身成为求职动机的信号，优于预测表现。因此，AI面试在传统信号失效处提供有效补充，同时将筛选成本转移至申请人。
原文摘要 · Abstract (English)
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.
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