The Gatekeeper Effect: The Implications of Pre-Screening, Self-Selection, and Bias for Hiring Processes
中文摘要
我们研究不确定性下决策过程中的筛选问题,重点关注增加一个额外筛选环节(通常称为“守门人”)所带来的影响。尽管我们的主要分析植根于就业市场招聘这一情境,但其中的原理与发现广泛适用于教育招生、患者医疗选择以及金融贷款审批等领域。守门人的作用是在产生重大成本之前评估申请人的适配性。我们的研究表明,尽管守门人旨在通过筛除较不可能被选中的候选人以简化筛选流程,但有时它们会无意中影响候选人自身的决策过程。我们探讨了引入守门人能够提升或阻碍这些流程效率的条件。此外,我们还考察了守门策略如何加以调整以影响筛选决策的准确性。我们的研究还扩展到守门行为受历史偏误影响的场景,尤其是在招聘等竞争性情境中。我们发现,面对统计上存在偏误的守门过程的候选人更有可能退出求职申请过程,从而使前述历史偏误得以延续。本研究表明,平权行动等措施能够有效纠正这些偏误。尽管以招聘为核心,但我们研究的洞见与方法对筛选与守门具有核心作用的广泛领域具有重要意义。本文由 Nicolas Stier-Moses 接受,属于“人与算法连接”特刊。资助:谨此感谢哈佛大学数学科学与应用中心(CMSA)以及以色列科技部(Yitzhak Shamir 奖学金)的资助。
Abstract
We study the problem of screening in decision-making processes under uncertainty, while focusing on the impact of adding an additional screening stage, commonly known as a “gatekeeper.” Although our main analysis is rooted in the context of job market hiring, the principles and findings are broadly applicable to areas such as educational admissions, patient healthcare selection, and financial loan approvals. The gatekeeper’s role is to assess applicant suitability before significant costs are incurred. Our study reveals that although gatekeepers are designed to streamline selection processes by filtering out the candidates who are less likely to be selected, sometimes they inadvertently affect the candidate’s own decision-making process. We explore the conditions under which the introduction of a gatekeeper can enhance or impede the efficiency of these processes. Additionally, we consider how gatekeeping strategies can be adapted to influence the accuracy of selection decisions. Our research also extends to scenarios in which gatekeeping is influenced by historical biases, particularly in competitive settings like hiring. We discover that candidates confronted with a statistically biased gatekeeping process are more likely to withdraw from the job application process, thereby perpetuating the previously mentioned historical biases. The study suggests that measures such as affirmative action can effectively address these biases. Although centered on hiring, the insights and methodologies from our study have significant implications for a wide range of fields to which screening and gatekeeping are integral. This paper was accepted by Nicolas Stier-Moses, Special Issue on the Human-Algorithm Connection. Funding: Financial support from the Center of Mathematical Sciences and Applications (CMSA) at Harvard University and the Ministry of Science and Technology of Israel (Yitzhak Shamir Fellowship) is gratefully acknowledged.