Race-conscious admissions algorithms and the law - Alexandra Chouldechova; Daniel J. Hemel (Proceedings of the National Academy of Sciences, 2026)
In recent years, colleges and universities have begun to use machine learning (ML) systems to inform admissions decisions. Meanwhile, in the 2023 case Students for Fair Admissions, Inc. v. President and Fellows of Harvard College , the Supreme Court held that colleges and universities may not make admissions decisions “on the basis of race.” These parallel developments—the rise of ML in admissions and the fall of race-based affirmative action—will force educational institutions, and ultimately courts, to confront the difficult question of what it means for ML systems to differentiate “on the basis of race.” We begin by mapping the Students for Fair Admissions decision onto different uses of race in predictive AI. We distinguish between “first-order” and “second-order” race consciousness at both the training and predictive phases of machine learning, and we argue that each category of race consciousness raises distinct legal and normative issues. We go on to show that the Students for Fair Admissions decision potentially permits—and even endorses—certain forms of race consciousness. Our analysis is grounded in the observation that the process of developing ML-based systems enables policymakers to calibrate decision making algorithms much more precisely and explicitly in response to specific criticisms of race-conscious affirmative action.