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