We investigate the role of the initial screening order (ISO) in candidate screening processes, such as employee hiring and academic admissions. The ISO refers to the order in which the screener evaluates the candidate pool. 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: the best-$k$, where the screener selects the $k$ best candidates, and the good-$k$, where the screener selects the $k$ first good-enough candidates. To study the impact of the ISO, we introduce a human-like screener and compare it to its algorithmic counterpart. The human-like screener is conceived to be inconsistent over time due to fatigue. Our analysis shows that the ISO, in particular, 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 the ISO. We report extensive simulated experiments exploring the parameters of the best-$k$ and good-$k$ problem formulations both for the algorithmic and human-like screeners. This work is motivated by a real world candidate screening problem studied in collaboration with a large European company.

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