Are your views on the list of 'domestic terrorism' indicators?
Minority report: FBI seeks AI for political watch list
Documents show the FBI seeking AI tools to flag Americans before they act, as the terror watchlist's focus moves toward domestic dissent.
Updated List of Criminal DHS Agents_08172026
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.
US Law Enforcement Warns of ‘Anti-Tech Extremism’ as AI Hatred Grows
As Americans stew over the looming risk of job-stealing AI and data centers in their back yards, the feds are raising the alarm about a new category of threat, documents obtained by WIRED show.
764: The Intersection of Terrorism, Violent Extremism, and Child Sexual Exploitation
Sign up for our monthly newsletter to stay updated about our work
DHS Criminal Agents - 7/20/26 Update
Scaring People into Supporting Backdoors - Schneier on Security
Back in 1998, Tim May warned us of the “Four Horsemen of the Infocalypse”: “terrorists, pedophiles, drug dealers, and money launderers.” I tended to cast it slightly differently. This is me from 2005: Beware the Four Horsemen of the Information Apocalypse: terrorists, drug dealers, kidnappers, and child pornographers. Seems like you can scare any public into allowing the government to do anything with those four. Which particular horseman is in vogue depends on time and circumstance. Since the terrorist attacks of 9/11, the US government has been pushing the terrorist scare story. Recently, it seems to have switched to pedophiles and child exploitation. It began in September, with a long ...
Disrupting Dark Networks
Disrupting Dark Networks focuses on how social network analysis can be used to craft strategies to track, destabilize and disrupt covert and illegal networks. The book begins with an overview of the key terms and assumptions of social network analysis and various counterinsurgency strategies. The next several chapters introduce readers to algorithms and metrics commonly used by social network analysts. They provide worked examples from four different social network analysis software packages (UCINET, NetDraw, Pajek and ORA) using standard network data sets as well as data from an actual terrorist network that serves as a running example throughout the book. The book concludes by considering the ethics of and various ways that social network analysis can inform counterinsurgency strategizing. By contextualizing these methods in a larger counterinsurgency framework, this book offers scholars and analysts an array of approaches for disrupting dark networks.
Federal Immigration Agent Shootings
An aggregated dataset of shootings by agents of Immigration and Customs Enforcement and Customs and Border Protection.
Remotely Coerced Violence: 764, The Com Network, and the Hybridization of Threats - Combating Terrorism Center at West Point
Abstract: This article examines 764 and the wider Com Network as a case study in remotely coerced violence and the hybridization of contemporary terrorist and violent extremist threats. It argues that nihilistic violent extremism is best understood as a victim-driven, youth-centered online ecosystem in which status, belonging, and identity are earned through the production, circulation, … Continued
Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects
AI that anticipates where crimes are likely to occur and who might commit them has a troubling track record. Democratic accountability could shine a light on the technology and how it’s used.
Predictive policing AI is on the rise − making it accountable to the public could curb its harmful effects
AI that anticipates where crimes are likely to occur and who might commit them has a troubling track record. Democratic accountability could shine a light on the technology and how it’s used.
Predictive Policing and the Politics of Patterns
Abstract Patterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns.
Data Center Watch
Free weekly updates on the political risks facing American data center projects
