49 Pages Posted: 26 Jul 2015
Date Written: 2014
In early 2013, U.S. Immigration and Customs Enforcement (“ICE”) deployed nationwide a new automated risk assessment tool to help determine whether to detain or release noncitizens pending their deportation proceedings. Adapted from similar evidence-based criminal justice reforms that have reduced pretrial detention, ICE’s initiative now represents the largest pre-hearing risk assessment experiment in U.S. history — potentially impacting over 400,000 individuals per year. However, to date little information has been released regarding the risk assessment algorithm, processes, and outcomes.
This article provides the first comprehensive examination of ICE’s risk assessment initiative, based on public access to ICE methodology and outcomes as a result of Freedom of Information Act requests. This article argues that immigration risk assessment in its current form will not reduce current over-detention trends. The unique aspects of immigration enforcement, laws, and the impacted population will likely frustrate accurate calibration of the risk tool, and effective implementation of even a calibrated tool — in turn frustrating constructive impact of ICE’s risk assessment initiative on over-detention. Consequently, the immigration risk assessment may only add a scientific veneer to enforcement that remains institutionally predisposed towards detention and control.
Additionally, this article argues that even if more accurate, evidence-based immigration detention were achieved under a future risk assessment regime, it would nonetheless likely be accompanied by several disadvantages. Particularly, risk assessment could facilitate a transition from mass detention to mass supervision of an even wider net of supervisees, by justifying lesser deprivations of liberty such as electronic supervision.
Suggested Citation: Suggested Citation
Noferi, Mark and Koulish, Robert, The Immigration Detention Risk Assessment (2014). Georgetown Immigration Law Review, Vol. 29, No. 45, 2014. Available at SSRN: https://ssrn.com/abstract=2635652