Entrofy Your Cohort: A Data Science Approach to Candidate Selection
Selecting a cohort from a set of candidates is a common task within and beyond academia. Admitting students, awarding grants, choosing speakers for a conference are situations where human biases may affect the make-up of the final cohort. We propose a new algorithm, Entrofy, designed to be part of a larger decision making strategy aimed at making cohort selection as just, quantitative, transparent, and accountable as possible. We suggest this algorithm be embedded in a two-step selection procedure. First, all application materials are stripped of markers of identity that could induce conscious or sub-conscious bias. During blind review, the committee selects all applicants, submissions, or other entities that meet their merit-based criteria. This often yields a cohort larger than the admissible number. In the second stage, the target cohort can be chosen from this meritorious pool via a new algorithm and software tool. Entrofy optimizes differences across an assignable set of categories selected by the human committee. Criteria could include gender, academic discipline, experience with certain technologies, or other quantifiable characteristics. The Entrofy algorithm yields the computational maximization of diversity by solving the tie-breaking problem with provable performance guarantees. We show how Entrofy selects cohorts according to pre-determined characteristics in simulated sets of applications and demonstrate its use in a case study. This cohort selection process allows human judgment to prevail when assessing merit, but assigns the assessment of diversity to a computational process less likely to be beset by human bias. Importantly, the stage at which diversity assessments occur is fully transparent and auditable with Entrofy. Splitting merit and diversity considerations into their own assessment stages makes it easier to explain why a given candidate was selected or rejected.