An aggregated dataset of shootings by agents of Immigration and Customs Enforcement and Customs and Border Protection.
New Study Explores Why US Latinos Join ICE and Border Patrol - Latino Rebels
A new study found that most of the agents joined because they wanted a stable job, and didn’t see many other ways of getting one.
They Walk Among Us - Criminal ICE and Border Agents — Ohio Immigrant Alliance
The Ohio Immigrant Alliance released “They Walk Among Us: A List of 59 Sexually and Physically Abusive ICE and Border Agents,” an analysis of sexual and violent crimes committed by ICE and Border Patrol agents. It report documents 59 cases of violent and/or sexual criminal acts by DHS employees.
ICE Detention Trends | Vera Institute of Justice
Every year, U.S. Immigration and Customs Enforcement (ICE) subjects hundreds of thousands of people to civil immigration detention—a practice that is unjust, inhumane, and completely unnecessary. It does so under a veil of secrecy, sharing only limited, often error-prone statistics with the public. ICE’s failure to regularly release accurate, complete, and accessible data to the public enables it to operate its multi-billion-dollar detention network with impunity and little oversight.
Amid a big hiring push, police experts question ICE officer vetting
The recent fatal shootings by ICE officers in Texas and Maine come at a time when the federal agency has hired a huge number of officers quickly. Some law enforcement experts worry that this kind of a hiring spree can lead police agencies to cut corners in vetting new hires.
Deportation Data Project
The Deportation Data Project obtains, posts, and analyzes internal U.S. government immigration enforcement data via public records litigation.
Know Your Rights | Enforcement at the Airport | ACLU
At the border, you are likely to encounter Customs and Border Protection (CBP) officers, and you may encounter Homeland Security Investigations (HSI) agents. HSI is part of U.S. Immigration and Customs Enforcement (ICE). Know your rights in these scenarios.
Department of Homeland Security AI Use Case Inventory
All data presented in this tracker is drawn directly from the DHS AI Use Case Inventory published by the Department of Homeland Security. The information is reproduced as provided in the source file and has not been independently verified, including statements on safety and impact. This presentation may contain errors or omissions. Users should consult the original DHS source file for authoritative data. The comparison view identifies differences between the 2024 Inventory July Revision and 2025 Inventory releases; some field differences reflect changes in the reporting format rather than substantive changes to the use cases.
Latinxs in <i>La Migra</i> : Why They Join and Why It Matters
Once an exclusively white enterprise, the last forty-five years have witnessed the emergence of a disproportionately Latinx immigration law enforcement workforce. This article addresses the question of why Latinxs elect to work for agencies that have systematically targeted the ethnic communities to which they belong. Where existing scholarship has often implied Latinxs may self-select into immigration law enforcement due to a lack of identification with the immigrant-experience, a dissociation with ethnic identity, and generally restrictionist immigration attitudes, this article finds little empirical evidence to support such an assumption. Analysis of interviews with sixty-one Latinx Immigration and Customs Enforcement (ICE) agents across Arizona, California, and Texas reveals, instead, Latinxs elect to work in immigration law enforcement in service of economic self-interest and survival, with “money,” “a good job,” and “benefits” cited as the primary motivation(s) behind applying for and accepting a job in immigration. This pattern holds irrespective of individual agents’ levels of identification with the immigrant-experience and particular attitudes toward immigration, and suggests a diversity in the demographics of immigration law enforcement agencies that extends beyond mere race and ethnicity, to include a diversity of perspective and potential for empathy.
Latinxs in <i>La Migra</i> : Why They Join and Why It Matters
Once an exclusively white enterprise, the last forty-five years have witnessed the emergence of a disproportionately Latinx immigration law enforcement workforce. This article addresses the question of why Latinxs elect to work for agencies that have systematically targeted the ethnic communities to which they belong. Where existing scholarship has often implied Latinxs may self-select into immigration law enforcement due to a lack of identification with the immigrant-experience, a dissociation with ethnic identity, and generally restrictionist immigration attitudes, this article finds little empirical evidence to support such an assumption. Analysis of interviews with sixty-one Latinx Immigration and Customs Enforcement (ICE) agents across Arizona, California, and Texas reveals, instead, Latinxs elect to work in immigration law enforcement in service of economic self-interest and survival, with “money,” “a good job,” and “benefits” cited as the primary motivation(s) behind applying for and accepting a job in immigration. This pattern holds irrespective of individual agents’ levels of identification with the immigrant-experience and particular attitudes toward immigration, and suggests a diversity in the demographics of immigration law enforcement agencies that extends beyond mere race and ethnicity, to include a diversity of perspective and potential for empathy.
ICE violence against women is largely untracked. One advocacy group is working to change that.
Immigration agents with prior records of sexual and gender-based violence have drawn attention in Trump’s second term amid questions about training and vetting.
How to Film ICE
Filming federal agents in public is legal, but avoiding a dangerous—even deadly—confrontation isn’t guaranteed. Here’s how to record ICE and CBP agents as safely as possible and have an impact.
Exclusive: FBI’s New Political Pre-Crime Center
Are your views on the list of 'domestic terrorism' indicators?
By the numbers: the latest ICE and CBP data on arrests, detentions and deportations in the US
The Guardian has reviewed figures from Immigration and Customs Enforcement and Customs and Border Protection since Trump’s inauguration
Community Alert: Immigration Arrests at Airports
This resource provides travel safety tips and other resources for immigrants traveling through U.S. airports.
Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement
There are widespread concerns about the use of artificial intelligence in law enforcement. Predictive policing and risk assessment are salient examples. Worries include the accuracy of forecasts that guide both activities, the prospect of bias, and an apparent lack of operational transparency. Nearly breathless media coverage of artificial intelligence helps shape the narrative. In this review, we address these issues by first unpacking depictions of artificial intelligence. Its use in predictive policing to forecast crimes in time and space is largely an exercise in spatial statistics that in principle can make policing more effective and more surgical. Its use in criminal justice risk assessment to forecast who will commit crimes is largely an exercise in adaptive, nonparametric regression. It can in principle allow law enforcement agencies to better provide for public safety with the least restrictive means necessary, which can mean far less use of incarceration. None of this is mysterious. Nevertheless, concerns about accuracy, fairness, and transparency are real, and there are tradeoffs between them for which there can be no technical fix. You can't have it all. Solutions will be found through political and legislative processes achieving an acceptable balance between competing priorities.
Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement
There are widespread concerns about the use of artificial intelligence in law enforcement. Predictive policing and risk assessment are salient examples. Worries include the accuracy of forecasts that guide both activities, the prospect of bias, and an apparent lack of operational transparency. Nearly breathless media coverage of artificial intelligence helps shape the narrative. In this review, we address these issues by first unpacking depictions of artificial intelligence. Its use in predictive policing to forecast crimes in time and space is largely an exercise in spatial statistics that in principle can make policing more effective and more surgical. Its use in criminal justice risk assessment to forecast who will commit crimes is largely an exercise in adaptive, nonparametric regression. It can in principle allow law enforcement agencies to better provide for public safety with the least restrictive means necessary, which can mean far less use of incarceration. None of this is mysterious. Nevertheless, concerns about accuracy, fairness, and transparency are real, and there are tradeoffs between them for which there can be no technical fix. You can't have it all. Solutions will be found through political and legislative processes achieving an acceptable balance between competing priorities.