论文标题
在不确定性下采取数据驱动的平权行动政策
Towards Data-Driven Affirmative Action Policies under Uncertainty
论文作者
论文摘要
在本文中，我们在集中式系统下研究大学录取，该系统使用成绩和标准化考试成绩与申请人与大学课程相匹配。我们考虑寻求增加代表性不足的申请人人数的平权行动政策。由于必须在申请期开始之前宣布此类政策，因此申请每个计划的学生的分数分布存在不确定性。这对决策者构成了艰巨的挑战。我们探讨了使用经过历史数据训练的预测模型来帮助优化此类政策参数的可能性。
In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. We consider affirmative action policies that seek to increase the number of admitted applicants from underrepresented groups. Since such a policy has to be announced before the start of the application period, there is uncertainty about the score distribution of the students applying to each program. This poses a difficult challenge for policy-makers. We explore the possibility of using a predictive model trained on historical data to help optimize the parameters of such policies.
