Title: Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes Show Chinese title

Title: 算法招聘与多样性：降低人机算法相似性以取得更好成果

Abstract: Algorithmic tools are increasingly used in hiring to improve fairness and diversity, often by enforcing constraints such as gender-balanced candidate shortlists. However, we show theoretically and empirically that enforcing equal representation at the shortlist stage does not necessarily translate into more diverse final hires, even when there is no gender bias in the hiring stage. We identify a crucial factor influencing this outcome: the correlation between the algorithm's screening criteria and the human hiring manager's evaluation criteria -- higher correlation leads to lower diversity in final hires. Using a large-scale empirical analysis of nearly 800,000 job applications across multiple technology firms, we find that enforcing equal shortlists yields limited improvements in hire diversity when the algorithmic screening closely mirrors the hiring manager's preferences. We propose a complementary algorithmic approach designed explicitly to diversify shortlists by selecting candidates likely to be overlooked by managers, yet still competitive according to their evaluation criteria. Empirical simulations show that this approach significantly enhances gender diversity in final hires without substantially compromising hire quality. These findings highlight the importance of algorithmic design choices in achieving organizational diversity goals and provide actionable guidance for practitioners implementing fairness-oriented hiring algorithms. Show Chinese abstract

Abstract: 算法工具越来越多地被用于招聘中以提高公平性和多样性，通常通过实施性别平衡的候选人短名单等约束条件来实现。然而，我们从理论和实证上表明，在短名单阶段强制实现平等代表并不一定转化为最终雇佣中更高的多样性，即使在雇佣阶段不存在性别偏见的情况下也是如此。我们确定了一个影响这一结果的关键因素：算法筛选标准与人类招聘经理评估标准之间的相关性——相关性越高，最终雇佣的多样性越低。通过分析来自多家技术公司的近80万份工作申请的大规模实证数据，我们发现当算法筛选与招聘经理的偏好高度一致时，强制执行平等的短名单只能带来有限的雇佣多样性改善。我们提出了一种互补的算法方法，专门设计用来通过选择可能被管理者忽视但根据其评估标准仍然具有竞争力的候选人来多样化短名单。实证模拟显示，这种方法显著提高了最终雇佣中的性别多样性，而不会大幅降低雇佣质量。这些研究结果强调了在实现组织多样性目标时算法设计选择的重要性，并为实施公平导向招聘算法的从业者提供了可操作的指导。