'We Ain't Seen Nothing Yet'--Trump's Mass Deportations Will Only Grow From Here Militias and far-right extremists believed they would be central to Trump's mass deportation plans. When Donald Trump won a second term as US president a year ago, members of violent militias and far-right extremist groups who had spent years boosting the lie that the 2020 election was rigged were ready to assist the president with delivering on one of his main campaign promises: mass deportations. "I'm willing to help," Richard Mack, a former sheriff who founded the far-right Constitutional Sheriffs and Peace Officers Association, told WIRED at the time, claiming he was in touch with Tom Homan, the man Trump installed as his "border czar." Tim Foley, head of the Arizona Border Recon, which describes itself as a "non-government organization," also told WIRED he was in contact with administration officials. William Teer, then head of the far-right Texas Three Percenters militia, wrote a letter to Trump offering his help.

A few benchmark datasets have received significant attention--e.g., COMP AS [

A searchable database of public records concerning use of force and misconduct by California law enforcement officers -- some 1.5 million pages from nearly 700 law enforcement agencies -- is now available to the public. The Police Records Access Project, a database built by UC Berkeley and Stanford University, is being published by the Los Angeles Times, San Francisco Chronicle, KQED and CalMatters. It will vastly expand public access to internal affairs records that show how law enforcement agencies throughout the state handle misconduct allegations and uses of police force that result in death or serious injury. The database currently includes records from nearly 12,000 cases. The database is the product of years of work by a multidisciplinary team of journalists, data scientists, lawyers and civil liberties advocates, led by the Berkeley Institute for Data Science (BIDS), UC Berkeley Journalism's Investigative Reporting Program (IRP) and Stanford University's Big Local News.

State Sen. John Bramnick joins'Fox & Friends' to discuss the upcoming meeting with Gov. Phil Murphy and officials over mysterious drone sightings in their state. Two years ago, a Chinese balloon the size of three school buses hovered 60,000 feet in the air, drifting across the continental U.S. for seven days. It passed over sensitive security areas, including Malmstrom Air Force Base in Great Falls, Montana, that's home to stockpiles of missiles and nuclear defense infrastructure. Only after it was shot down did we learn this "civilian research airship" that President Biden claimed "was not a major security breach" was communicating with China through an American internet service provider and equipped with thousands of pounds of equipment, including a "massive surveillance payload." One would think the President of the United States and our nation's federal law enforcement agencies would have learned a lesson from this blatant security breach.

In recent years, urban safety has become a paramount concern for city planners and law enforcement agencies. Accurate prediction of likely crime occurrences can significantly enhance preventive measures and resource allocation. However, many law enforcement departments lack the tools to analyze and apply advanced AI and ML techniques that can support city planners, watch programs, and safety leaders to take proactive steps towards overall community safety. This paper explores the effectiveness of ML techniques to predict spatial and temporal patterns of crimes in urban areas. Leveraging police dispatch call data from San Jose, CA, the research goal is to achieve a high degree of accuracy in categorizing calls into priority levels particularly for more dangerous situations that require an immediate law enforcement response. This categorization is informed by the time, place, and nature of the call. The research steps include data extraction, preprocessing, feature engineering, exploratory data analysis, implementation, optimization and tuning of different supervised machine learning models and neural networks. The accuracy and precision are examined for different models and features at varying granularity of crime categories and location precision. The results demonstrate that when compared to a variety of other models, Random Forest classification models are most effective in identifying dangerous situations and their corresponding priority levels with high accuracy (Accuracy = 85%, AUC = 0.92) at a local level while ensuring a minimum amount of false negatives. While further research and data gathering is needed to include other social and economic factors, these results provide valuable insights for law enforcement agencies to optimize resources, develop proactive deployment approaches, and adjust response patterns to enhance overall public safety outcomes in an unbiased way.

For the past three months, a small encrypted group chat of Latin American officials who investigate online child-exploitation cases has been lighting up with reports of raids, arrests, and rescued minors in half a dozen countries. The successes are the result of a recent trial of a facial-recognition tool given to a group of Latin American law-enforcement officials, investigators, and prosecutors by the American company Clearview AI. During a five-day operation in Ecuador in early March, participants from 10 countries including Argentina, Brazil, Colombia, the Dominican Republic, El Salvador, and Peru were given access to Clearview's technology, which allows them to upload images and run them through a database of billions of public photos scraped from the Internet. "Normally it takes at least several days for a child to be identified, and sometimes there are victims that have not been identified for years," says Guillermo Galarza Abizaid, the vice president in charge of partnerships and law enforcement at the Virginia-based nonprofit International Centre for Missing and Exploited Children (ICMEC), which organized the event. The group used the facial-recognition tool to analyze a total of 2,198 images and 995 videos, hundreds of them from cold cases.

Leading artificial intelligence companies including OpenAI, Microsoft, Google, Meta and others have jointly pledged to prevent their AI tools from being used to exploit children and generate child sexual abuse material (CSAM). The initiative was led by child-safety group Thorn and All Tech Is Human, a non-profit focused on responsible tech. The pledges from AI companies, Thorn said, "set a groundbreaking precedent for the industry and represent a significant leap in efforts to defend children from sexual abuse as a feature with generative AI unfolds." The goal of the initiative is to prevent the creation of sexually explicit material involving children and take it off social media platforms and search engines. More than 104 million files of suspected child sexual abuse material were reported in the US in 2023 alone, Thorn says.

At least one person has been killed and two were injured after a drone crashed into an apartment building in Russia's Belgorod region. Authorities there said they evacuated more than 3,500 children following a spate of Ukrainian attacks. Russia targeted Ukraine's key energy infrastructure in escalated shelling, firing dozens of drones and missiles and injuring at least six people, according to Ukrainian officials. Ukraine's Air Force said 99 missiles and drones were fired, but 84 of them were intercepted. Ukraine introduced emergency blackouts in three regions – Dnipropetrovsk, Zaporizhia and Kirovograd – because of the attacks, and the authorities urged consumers in other regions to limit electricity consumption.

The use of police drones is "poised to explode" in the next year as law enforcement takes advantage of the technology's proliferation, leaving public regulation and transparency efforts in danger of being caught woefully behind, civil rights advocates warn. "A world where flying robotic police cameras constantly crisscross our skies is one we have never seen before," Jay Stanley, senior policy analyst with the American Civil Liberties Union, wrote in a report released Thursday. "Yet there are strong reasons to believe that such a world may be coming faster than most people realize." At least 1,400 police departments across the country are using drones in some fashion, but only 15 have obtained waivers from the Federal Aviation Administration to fly their drones beyond the visual line of sight, or BVLOS, of operators. That means the vast majority of departments are still limited in the types of calls they can respond to with drones.