About | HeinOnline Law Journal Library | HeinOnline Law Journal Library | HeinOnline
LOG IN
LOG IN

94 N.Y.U. L. Rev. 544 (2019)
Challenging Racist Predictive Policing Algorithms under the Equal Protection Clause

handle is hein.journals/nylr94 and id is 558 raw text is: CHALLENGING RACIST PREDICTIVE POLICING ALGORITHMS UNDER THE EQUAL PROTECTION CLAUSE RENATA M. O'DONNELL* Algorithms are capable of racism, just as humans are capable of racism. This is particularly true of an algorithm used in the context of the racially biased criminal justice system. Predictive policing algorithms are trained on data that is heavily infected with racism because that data is generated by human beings. Predictive policing algorithms are coded to delineate patterns in massive data sets and subse- quently dictate who or where to police. Because of the realities of America's crim- inal justice system, a salient pattern emerges from the racially skewed data Race is associated with criminality in the United States. Because of the black-box nature of machine learning, a police officer could naively presume that an algorithm's results are neutral; when they are, in fact, infected with racial bias. In this way, a machine learning algorithm is capable of perpetuating racist policing in the United States. An algorithm can exacerbate racist policing because of positive feedback loops, wherein the algorithm learns that it was correct in associating race and criminality and will rely more heavily on this association in its subsequent iterations. This Note is the first piece to argue that machine learning-based predictive policing algorithms are a facial, race-based violation of the Equal Protection Clause. There will be major hurdles for litigants seeking to bring an equal protection challenge to these algorithms, including attributing algorithmic decisions to a state actor and overcoming the proprietary protections surrounding these algorithms. However, if the courts determine that these hurdles eclipse the merits of an equal protection claim, the courts will render all algorithmic decisionmaking immune to equal pro- tection review. Such immunization would be a dangerous result, given that the gov- ernment is hurling a growing number of decisions into black-box algorithms. INTRODUCTION ..................................................... 545 I. MACHINE LEARNING-BASED PREDICTIVE POLICING ALGORITHMS AND THEIR PROBLEMS .................... 548 A. What Are Machine Learning Algorithms? ........... 549 * Copyright © 2019 by Renata M. O'Donnell. J.D. Candidate, 2019, New York University School of Law; B.A., 2016, University of Pennsylvania. I am grateful to Professor Barry Friedman, Professor Jessica Bulman-Pozen, and Professor Maria Ponomarenko for taking the time to answer my many questions, provide feedback, and read drafts of this Note. I would like to thank the Editorial Board of the New York University Law Review, particularly my editors Nicholas Baer, Alice Hong, and Benjamin Perotin, for the hard work they put in to edit this piece. Special thanks to Professor Kimberly Taylor-Thompson, Professor Tony Thompson, and Professor Randy Hertz for inspiring and cultivating my interest in criminal justice reform. Finally, thank you to the three greatest lawyers I know-my parents, Catherine and Neil T. O'Donnell, and my brother, Neil P. O'Donnell-for thoughtful criticism on this Note and for all of your love, support, and sacrifice. 544 Imaged with Permission of N.Y.U. Law Review

What Is HeinOnline?

HeinOnline is a subscription-based resource containing thousands of academic and legal journals from inception; complete coverage of government documents such as U.S. Statutes at Large, U.S. Code, Federal Register, Code of Federal Regulations, U.S. Reports, and much more. Documents are image-based, fully searchable PDFs with the authority of print combined with the accessibility of a user-friendly and powerful database. For more information, request a quote or trial for your organization below.



Short-term subscription options include 24 hours, 48 hours, or 1 week to HeinOnline.

Purchase Access

Contact us for annual subscription options:

Contact Us

Already a HeinOnline Subscriber?

Log In

profiles profiles most