Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability

TL;DR

This paper examines how changes in college admissions policies, especially the removal of race data, affect applicant ranking, diversity, and outcome variability using machine learning models amid evolving societal and legal contexts.

Contribution

It quantifies the impact of policy changes on applicant ranking and diversity, highlighting the increased arbitrariness and reduced diversity when race data is excluded from models.

Findings

Removing race data reduces diversity in top-ranked applicants.

Excluding race data increases outcome arbitrariness for most applicants.

Policy changes have a greater impact than other variables like intended majors.

Abstract

Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-2024 admissions cycle, these colleges faced unprecedented challenges. First, the number of applications has been steadily growing. Second, test-optional policies that have remained in place since the COVID-19 pandemic limit access to key information historically predictive of academic success. Most recently, longstanding debates over affirmative action culminated in the Supreme Court banning race-conscious admissions. Colleges have explored machine learning (ML) models to address the issues of scale and missing test scores, often via ranking algorithms intended to focus on 'top' applicants. However, the Court's ruling will force changes to these models, which were able to consider race as a factor in ranking. There is currently a poor understanding of how these mandated changes will shape applicant ranking algorithms, and, by extension, admitted classes. We seek to address this by quantifying the impact of different admission policies on the applications prioritized for review. We show that removing race data from a developed applicant ranking algorithm reduces the diversity of the top-ranked pool without meaningfully increasing the academic merit of that pool. We contextualize this impact by showing that excluding data on applicant race has a greater impact than excluding other potentially informative variables like intended majors. Finally, we measure the impact of policy change on individuals by comparing the arbitrariness in applicant rank attributable to policy change to the arbitrariness attributable to randomness. We find that any given policy has a high degree of arbitrariness and that removing race data from the ranking algorithm increases arbitrariness in outcomes for most applicants.