Early Predictability of Asylum Court Decisions

Proceedings of the ACM Conference on AI and the Law, 2017

9 Pages Posted: 2 Aug 2016 Last revised: 20 Apr 2020

See all articles by Matthew Dunn

Matthew Dunn

NYU Center for Data Science

Levent Sagun

Courant Institute of Mathematical Sciences

Hale Sirin

Johns Hopkins University

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France

Date Written: January 26, 2017

Abstract

In the United States, foreign nationals who fear persecution in their home country can apply for asylum under the Refugee Act of 1980. Over the past decade, legal scholarship has uncovered significant disparities in asylum adjudication by judge, by region of the United States in which the application is filed, and by the applicant’s nationality. These disparities raise concerns about whether applicants are receiving equal treatment under the law. Using machine learning to predict judges’ decisions, we document another concern that may violate our notions of justice: we are able to predict the final outcome of a case with 80% accuracy at the time the case opens using only information on the identity of the judge handling the case and the applicant’s nationality. Moreover, there is significant variation in the degree of predictability of judges at the time the case is assigned to a judge. We show that highly predictable judges tend to hold fewer hearing sessions before making their decision, which raises the possibility that early predictability is due to judges deciding based on snap or predetermined judgments rather than taking into account the specifics of each case. Early prediction of a case with 80% accuracy could assist asylum seekers in their applications.

Keywords: Immigration, Asylum, Machine Learning, Refugee

Suggested Citation

dunn, matthew and Sagun, Levent and Sirin, Hale and Chen, Daniel L., Early Predictability of Asylum Court Decisions (January 26, 2017). Proceedings of the ACM Conference on AI and the Law, 2017, Available at SSRN: https://ssrn.com/abstract=2816191

Matthew Dunn

NYU Center for Data Science ( email )

New York, NY
United States
6034941534 (Phone)

HOME PAGE: http://cds.nyu.edu/academics/ms-in-data-science/

Levent Sagun (Contact Author)

Courant Institute of Mathematical Sciences ( email )

251 Mercer Street
New York, NY - 10012
United States

Hale Sirin

Johns Hopkins University ( email )

Baltimore, MD 20036-1984
United States

Daniel L. Chen

Directeur de Recherche, Centre National de la Recherche Scientifique, Toulouse School of Economics, Institute for Advanced Study in Toulouse, University of Toulouse Capitole, Toulouse, France ( email )

Toulouse School of Economics
1, Esplanade de l'Université
Toulouse, 31080
France

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