In this paper we present the initial screening order problem, a crucial step within candidate screening. It involves a human-like screener with an objective to find the first k suitable candidates rather than the best k suitable candidates in a candidate pool given an initial screening order. The initial screening order represents the way in which the human-like screener arranges the candidate pool prior to screening. The choice of initial screening order has considerable effects on the selected set of k candidates. We prove that under an unbalanced candidate pool (e.g., having more male than female candidates), the human-like screener can suffer from uneven efforts that hinder its decision-making over the protected, under-represented group relative to the non-protected, over-represented group. Other fairness results are proven under the human-like screener. This research is based on a collaboration with a large company to better understand its hiring process for potential automation. Our main contribution is the formalization of the initial screening order problem which, we argue, opens the path for future extensions of the current works on ranking algorithms, fairness, and automation for screening procedures.

翻译：本文提出了初始筛选顺序问题，这是候选人筛选中的关键步骤。该问题涉及一个拟人化筛选者，其目标是在给定初始筛选顺序的情况下，从候选人池中找到前k名合适的候选人，而非最佳的k名候选人。初始筛选顺序代表了拟人化筛选者在筛选前对候选人池的排列方式。初始筛选顺序的选择会对选定的k名候选子集产生显著影响。我们证明，在候选人池不均衡的情况下（例如男性候选人多于女性候选人），拟人化筛选者可能因非均衡的努力而遭受影响，这阻碍了其对受保护的弱势群体（相对于非保护的强势群体）的决策。此外，在拟人化筛选者情境下，我们还证明了其他公平性结果。本研究基于与一家大型企业的合作，旨在更好地理解其招聘流程以实现潜在自动化。我们的主要贡献在于对初始筛选顺序问题的形式化定义，我们认为这为现有排序算法、公平性及筛选流程自动化研究的未来扩展开辟了道路。