Alexandra Chouldechova (Carnegie Mellon University – H. John Heinz III School of Public Policy and Management) and Daniel J. Hemel (New York University School of Law) have posted Race-Conscious Admissions Algorithms and the Law on SSRN. Here is the abstract:
In 2023, the U.S. Supreme Court held in Students for Fair Admissions v. Harvard that higher education institutions cannot admit students “on the basis of race.” This article addresses what it means for an admissions algorithm to operate on the basis of race. We develop a taxonomy of race consciousness in the algorithmic decision making context that provides lawyers and machine learning researchers with a shared vocabulary for exploring the implications of the Court’s ruling. 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 explain why the Court’s decision need not be read as a flat-out ban on all types of race consciousness in admissions, and why certain forms of race consciousness might even advance the goals of justices who voted to strike down affirmative action policies in SFFA.
Highly Recommended!
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