中文摘要
本文将招聘视为一个情境多臂老虎机问题：为了在持续的招聘过程中找到最优秀的员工，企业必须在“利用”（从已有经验证的业绩记录的群体中选拔）与“探索”（从代表性不足的群体中选拔，以了解其质量）之间取得平衡。然而，基于监督学习方法的现代招聘算法仅为“利用”而设计。与之不同，我们构建了一种重视“探索”的简历筛选算法，依据候选人在统计意义上的上行潜力对其进行评估。利用一家《财富》500强企业的专业服务招聘数据，我们表明，相较于该企业现有的做法，这一方法提高了入选面试的候选人的质量（以最终录用率衡量），同时也提高了人口统计特征的多样性。传统的基于监督学习的算法则未能同时实现这两点：这类算法提高了录用率，但选出的黑人和西班牙裔申请人要少得多。综合来看，我们的结果凸显了在开发可能兼具更高效率与公平性的决策算法时纳入“探索”的重要性。
Abstract
Abstract This article views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation” (selecting from groups with proven track records) with “exploration” (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm’s existing practices. The same is not true for traditional supervised learning-based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.