熵化您的队列：候选人选择的数据科学方法
Entrofy Your Cohort: A Data Science Approach to Candidate Selection
摘要：
从一组候选人中选择一组是学术界内外的一项共同任务。录取学生、发放助学金、为会议选择演讲者都是人类偏见可能影响最终群体构成的情况。我们提出了一种新的算法，熵，旨在成为一个更大的决策策略的一部分，旨在使队列选择尽可能公正，量化，透明和负责任。我们建议将此算法嵌入在一个两步选择过程。首先，所有申请材料都被剥夺了可能引起有意识或潜意识偏见的身份标记。在盲审期间，委员会选择所有符合其择优标准的申请人、提交材料或其他实体。这往往会产生一个比可接受的人数更大的队列。在第二阶段，可以通过新的算法和软件工具从这个有价值的池中选择目标队列。Entrofy优化了由人类委员会选择的一组可分配类别之间的差异。标准可以包括性别、学科、某些技术的经验或其他可量化的特征。熵算法通过解决具有可证明性能保证的平局打破问题来产生多样性的计算最大化。我们展示了Entrofy如何根据模拟应用程序集中的预定特征选择队列，并在案例研究中演示了其使用。这种群体选择过程允许人类判断在评估价值时占上风，但将多样性评估分配给不太可能受到人类偏见困扰的计算过程。重要的是，多样性评估发生的阶段是完全透明的，可以通过Entrofy进行审计。将优点和多样性考虑分为各自的评估阶段，可以更容易地解释为什么某个候选人被选中或拒绝。
英文摘要：
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.
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