We investigate the role of the initial screening order (ISO) in candidate screening processes, such as hiring and academic admissions. ISO refers to the order in which the screener sorts the candidate pool before the evaluation. It has been largely overlooked in the literature, despite its potential impact on the optimality and fairness of the chosen set, especially under a human screener. We define two problem formulations: best-$k$, where the screener chooses the $k$ best candidates, and good-$k$, where the screener chooses the first $k$ good-enough candidates. To study the impact of ISO, we introduce a human-like screener and compare to its algorithmic counterpart. The human-like screener is conceived to be inconsistent over time due to fatigue. Our analysis shows that the ISO under a human-like screener hinders individual fairness despite meeting group level fairness. This is due to the position bias, where a candidate's evaluation is affected by its position within ISO. We report extensive simulated experiments exploring the parameters of the problem formulations both for algorithmic and human-like screeners. This work is motivated by a real world candidate screening problem studied in collaboration with a large European company.

翻译：我们研究了初始筛选顺序（ISO）在候选人筛选流程（如招聘和学术招生）中的作用。ISO指筛选者在评估前对候选人池进行排序的顺序。尽管ISO可能对所选集合的最优性和公平性产生潜在影响（特别是在人工筛选者参与的情况下），但在现有文献中该问题基本被忽视。我们定义了两种问题形式化表述：最佳-k问题（筛选者选择k名最优候选人）和合格-k问题（筛选者选择前k名合格候选人）。为研究ISO的影响，我们引入了一种类人筛选者模型，并将其与算法化筛选者进行对比。类人筛选者被设计为因疲劳而随时间产生不一致性。分析表明，在类人筛选者条件下，尽管ISO能满足群体层面的公平性，但会阻碍个体公平性。这是由于位置偏差效应——候选人的评估结果受其在ISO中位置的影响。我们报告了针对算法化与类人筛选者两种情形下的问题参数探索性大规模模拟实验。本研究受与一家欧洲大型企业合作开展的真实候选人筛选问题驱动。