Towards Data-Driven Affirmative Action Policies under Uncertainty

TL;DR

This paper explores how predictive models can optimize affirmative action policies in university admissions under uncertainty about applicant score distributions.

Contribution

It introduces a data-driven approach to design and optimize affirmative action policies using historical data and predictive modeling.

Findings

Predictive models can improve policy outcomes under uncertainty.

Data-driven policies outperform static approaches.

Framework aids policymakers in decision-making.

Abstract

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.