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17 Colo. Tech. L.J. 131 (2018-2019)
How to Argue with an Algorithm: Lessons from the COMPAS-ProPublica Debate

handle is hein.journals/jtelhtel17 and id is 145 raw text is: HOW TO ARGUE WITH AN ALGORITHM: LESSONS FROM THE COMPAS- PROPUBLICA DEBATE ANNE L. WASHINGTON, PHD* The United States optimizes the efficiency of its growing criminal justice system with algorithms. However, legal scholars have overlooked how to frame courtroom debates about algorithmic predictions. In State v. Loomis, the defense argued that the court's consideration of risk assessments during sentencing was a violation of due process because the accuracy of the algorithmic prediction could not be verified. The Wisconsin Supreme Court upheld the consideration of predictive risk at sentencing because the assessment was disclosed and the defendant could challenge the prediction by verifying the accuracy of data fed into the algorithm. Was the court correct about how to argue with an algorithm? The Loomis court ignored the computational procedures that processed the data within the algorithm. How algorithms calculate data is equally as important as the quality of the data calculated. The arguments in Loomis revealed a need for new forms of reasoning to justify the logic of evidence-based tools. A data science reasoning could provide ways to dispute the integrity of predictive algorithms with arguments grounded in how the technology works. This article's contribution is a series of arguments that could support due process claims concerning predictive algorithms, specifically the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) risk assessment. As a comprehensive treatment, this article outlines the due process arguments in Loomis, analyzes arguments in an ongoing academic debate about COMPAS, and proposes alternative arguments based on the algorithm's organizational context. * Assistant Professor of Data Policy, Department of Applied Statistics, Social Science, and Humanities. Steinhardt School, New York University. This work draws on my 2016-17 fellowship at the Data & Society Research Institute in New York where I developed the concept of data science reasoning. Dr. David C. Morar, PhD, faithfully tracked the publication of new articles in 2016-17. I am grateful to my fellow D&S fellows Andrew Selbst, Julia Powles, and Rebecca Wexler. Many thanks to Jennifer Eaglin, Frank A. Pasquale, Siona Robin Listokin, David G. Robinson, Paul Ohm, Margaret Hu, the diligent law journal students at Colorado, as well as my colleagues at NYU and Data & Society. 131

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