What is it about?

Selective colleges in the U.S. receive tens of thousands of applications each year and aim to build a class that is both academically talented and diverse. Recent changes, including the Supreme Court's ban on affirmative action, have made this task even more challenging. Many colleges use machine learning tools to rank applicants and help admissions teams focus on the strongest candidates. Our study explores how removing race as a factor in these ranking tools affects diversity, academic merit, and arbitrariness in decisions. We found that without considering race, the pool of top-ranked applicants becomes less diverse, but it does not significantly improve their academic qualifications. Additionally, we discovered that admissions outcomes can be highly arbitrary—most applicants are not consistently ranked as "top" candidates across different policies.

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Why is it important?

This work is especially important now because of the recent Supreme Court decision banning race-conscious admissions. This decision forces colleges to change how they evaluate applicants, which could unintentionally reduce diversity in the admissions process. Our study contributed to investigating how these changes impact who gets prioritized for review and how arbitrary admissions decisions can be under different policies. By showing the consequences of removing certain sensitive data, our work helps colleges, policymakers, and the public better understand the trade-offs involved in these decisions. Our approach can be applied to explore the impact of other potential policy changes. This makes our study timely and a valuable tool for anticipating and addressing the effects of future policy shifts in the rapidly changing landscape of college admissions.

Perspectives

I hope this article draws attention to the importance of designing an equitable higher education system. Writing this paper was a great experience for me, and it raised an important question: How can we preserve diversity in higher education while building a system that is fairer, more inclusive, and helps close the educational opportunity gap? Moving forward, I hope to gain a deeper understanding of high-stakes decision-making processes in education.

Jinsook Lee
Cornell University

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This page is a summary of: Ending Affirmative Action Harms Diversity Without Improving Academic Merit, October 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3689904.3694706.
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