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: 


                          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

    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
         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.


Imaged with Permission of N.Y.U. Law Review

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