Towards Data-Driven Affirmative Action Policies under Uncertainty
This work addresses a specific problem for policy-makers in centralized university admissions systems, but it appears incremental as it applies existing predictive modeling techniques to a known domain.
The paper tackles the challenge of designing affirmative action policies for university admissions under uncertainty about applicant score distributions, by proposing to use predictive models trained on historical data to optimize policy parameters.
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.