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Since 2012, scoring algorithms created to manage risks in the United States penal system have been adopted by Immigration and Customs Enforcement (ICE) agencies across the United States. First developed as a mechanism to reduce anti-immigrant biases and to ensure the humane treatment of detainees, scoring algorithms suffered constant revisions to accommodate DHS enforcement priorities as well as the preferences and punitive biases of ICE agents. With the arrival of the Trump administration, a technology created to ensure the humane treatment of undocumented immigrants became central to the policy of criminalization of the immigration process. This book provides historical, qualitative, and quantitative evidence of the process that placed risk assessment technologies at the forefront of the anti-immigration battle.

Using very large data sets on immigration and detention proceedings obtained from DHS through multiple FOIA requests, this Brief reveals the inner workings of the risk classification algorithms (RCA) used to process tens of thousands of immigrants each day. Chapters examine the tension between risk algorithms and end users and explain how ICE officers' preferences shape the scoring properties of RCA used in immigration enforcement. Illustrating how scoring algorithms oppress immigrants of color, this book is interest policymakers, immigration scholars, lawyers, criminologists, political scientists, and university professors, graduate students, and undergraduate students in the behavioral sciences.