The Diverse Cohort Selection Problem

Abstract

How should a firm allocate its limited interviewing resources to select the optimal cohort of new employees from a large set of job applicants? How should that firm allocate cheap but noisy resume screenings and expensive but in-depth in-person interviews? We view this problem through the lens of combinatorial pure exploration (CPE) in the multi-armed bandit setting, where a central learning agent performs costly exploration of a set of arms before selecting a final subset with some combinatorial structure. We generalize a recent CPE algorithm to the setting where arm pulls can have different costs and return different levels of information. We then prove theoretical upper bounds for a general class of armpulling strategies in this new setting. We apply our general algorithm to a real-world problem with combinatorial structure: incorporating diversity into university admissions. We take real data from admissions at one of the largest US-based computer science graduate programs and show that a simulation of our algorithm produces a cohort with hiring overall utility while spending comparable budget to the current admissions process at that university.

Leximax Approximations and Representative Cohort Selection

“…In the setting of selecting representative data, prior works define metrics for diversity [26], and give algorithms for diverse data selection and summarization [7,20]. For selecting individuals from a larger pool, prior works on cohort selection and multi-winner elections have studied individual guarantees of fairness [2] as well as group parity goals of diversity [5,8,29]. Other works have examined how bias and variance may affect different groups differently during a selection process and fairness amounts to remedying implicit bias and variance in the selection process for different groups of individuals [13,19].…”