发表机构
Carnegie Mellon University; Erasmus University Rotterdam（卡内基梅隆大学; 鹿特丹伊拉斯姆斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过7万名求职者的实地实验发现，AI语音代理面试比人类招聘更结构化一致，能收集更多招聘相关信息，使求职者录用概率高12%且提升入职留存，同时不降低被录用者生产力，证明AI自动化信息收集可提升决策质量。
AI 中文摘要
本文研究AI自动化能否通过降低信息收集时的方差来改善组织结果。我们开展了一项大规模自然实地实验，将7万名求职者随机分配为接受人类招聘人员面试或AI语音代理面试两组。两种情境下均由人类招聘人员评估面试并做出招聘决策。接受AI代理面试的求职者获得录用通知的概率高出12%，且这些收益转化为更多入职人数和更高的员工留存率，同时被录用员工的生产力未出现下降。对面试记录的分析显示，AI语音代理实现了可控方差：其面试更具结构性和一致性，同时仍能对个体求职者做出响应，这与收集到的更多与招聘相关的信息相关联。这些结果表明，用AI实现信息收集自动化可通过标准化提升决策质量。
英文摘要
We study AI agents as information-collection technologies: automated systems that elicit decision-relevant signals from humans through live interactions. We test how such AI automation impacts information collection and organizational outcomes using a natural field experiment with 70,000 applicants applying for real jobs. Applicants were randomly assigned to be interviewed by either human recruiters or AI voice agents. Afterward, human recruiters evaluate the interviews and make hiring decisions. Applicants interviewed by AI agents are 12% more likely to receive job offers, and these gains translate into higher job starts and worker retention, with no decline in the productivity of hired workers. Analyzing interview transcripts reveals that AI voice agents achieve controlled variance: their interviews are more structured and consistent while remaining responsive to individual applicants, which is associated with more hiring-relevant information collected. Our results suggest that a key advantage of AI automation lies in environments where information collection is delegated across many human workers and repeated such that variance in task execution becomes noise in decision-relevant signals, which AI compresses through adaptive standardization.
