用AI助手帮求职者理解招聘决策，提升透明度与信任感。
Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions
- 构建多智能体系统，用大模型为求职者解释录用逻辑。
- 20名求职者测试显示，新系统更可信、公平且有行动指导性。
- 适合关注招聘透明化与可解释AI的从业者和研究者。
求职招聘中，传统筛选方式常缺乏透明度，候选人很少获得录用决策的充分解释，无论是人工招聘还是黑箱式申请跟踪系统（ATS）。为此，我们提出一种基于大语言模型（LLMs）的多智能体AI系统，旨在引导求职者理解招聘过程。通过两阶段探索性研究，结合四位活跃求职者的反馈，我们设计并开发了原型系统；随后开展深度定性用户研究，对20位活跃求职者进行一对一访谈评估。结果表明，参与者认为该系统在可操作性、可信度和公平性方面显著优于传统方法。研究还揭示了影响用户体验的关键因素，为跨领域构建以用户为中心的可解释多智能体AI系统提供了设计启示。
原文摘要 · Abstract (English)
During job recruitment, traditional applicant selection methods often lack transparency. Candidates are rarely given sufficient justifications for recruiting decisions, whether they are made manually by human recruiters or through the use of black-box Applicant Tracking Systems (ATS). To address this problem, our work introduces a multi-agent AI system that uses Large Language Models (LLMs) to guide job seekers during the recruitment process. Using an iterative user-centric design approach, we first conducted a two-phased exploratory study with four active job seekers to inform the design and development of the system. Subsequently, we conducted an in-depth, qualitative user study with 20 active job seekers through individual one-to-one interviews to evaluate the developed prototype. The results of our evaluation demonstrate that participants perceived our multi-agent recruitment system as significantly more actionable, trustworthy, and fair compared to traditional methods. Our study further helped us uncover in-depth insights into factors contributing to these perceived user experiences. Drawing from these insights, we offer broader design implications for building user-aligned, multi-agent explainable AI systems across diverse domains.
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