GLOBAL AI DEVELOPMENTS

The global race for AI implementation & improvement

• US-China AI race drives new containment strategies

• AI regulation strategies Latin American policymakers should adopt

• UAE joins the AI superpower race with its 5-gigawatt Stargate project

• UAE accelerates AI push with ambitious “Cognitive Cities” plan

• Gulf AI deals boost US dominance in global tech race

REGULATION & SAFETY

Law, lobbying & potential risks

• Health systems urge government action to support AI transparency

• GOP budget bill seeks to reshape AI regulation and roll back climate incentives

• House Republicans move to slash solar incentives amid AI energy crunch

• Judge declines First Amendment defense in AI harm case against Google and Character.AI

• Judge weighs AI use penalties for lawyers in prison case

AI Governance

The latest government programs & implementations

• Facial recognition tech aids in New Orleans inmate search, civil libertarians concerned

• NSF funding faces 55% cut, alarming university researchers

• FDA taps generative AI to tackle scientific data deluge and boost efficiency

• SUNY offers free tech programs to adult community college students

• Salem schools pilot AI to detect weapons, smoke, and other threats in real time

DEFENSE

Weapons, tech, research & contracts

• European defense tech fund receives €40m boost from EIF

• How AI is eliminating the trade-off between stability and performance

• Microsoft confirms AI aid to Israeli military amid Gaza war

• The AI arms race between global superpowers is a risky gamble with existential stakes

• Google files reveal concerns over Project Nimbus control in Israel

Funding AI education, industry immersion, and career assistance to those who have served our nation.

What’s happening in AI policy right now

AI surveillance becomes reality in American cities

Law enforcement agencies are quietly deploying AI policing systems that can predict crime

A prison break in New Orleans last month offered a glimpse into how dramatically police work has changed. Within minutes of inmates escaping from a detention center, facial recognition cameras had identified and tracked the fugitives across the city. What once might have required days of manhunting was resolved in hours, thanks to Project NOLA's network of 5,000 surveillance cameras.

This success story, praised by New Orleans Police Superintendent Anne Kirkpatrick as critical to public safety, represents just one facet of a broader transformation happening across American law enforcement. Police departments are embracing AI-powered surveillance systems that promise to predict crime before it happens and identify threats in real-time. The technology works remarkably well; the question is whether we're comfortable with the world it's creating.

When Police Work Becomes Minority Report

The shift from reactive to predictive policing is happening faster than most people realize. New Orleans police recently conducted a secret facial recognition program using private cameras to identify suspects in real-time, sending immediate alerts to officers' phones when matches were detected. The program, which led to dozens of arrests, operated without required oversight and potentially violated city ordinances designed to regulate such surveillance.

This isn't an isolated incident. Countries around the world are implementing AI systems designed to flag "high risk" individuals before they commit crimes. The UK plans to use personal data, including mental health histories, to identify potential criminals. Argentina has established an Artificial Intelligence Unit for Security focused on crime prediction. Canadian police in Toronto and Vancouver deploy predictive policing systems alongside facial recognition tools.

The technology promises impressive results. AI can analyze patterns in vast datasets to identify locations where crimes are likely to occur, recognize faces in crowds, and even detect weapons in surveillance footage. But it also represents a fundamental shift in how we think about policing - from investigating crimes after they happen to flagging people as potential criminals based on algorithmic assessments.

The School Safety Calculus

This predictive approach is extending beyond traditional law enforcement. Salem High School will implement an AI weapons detection system in 2025, spending $47,000 annually to monitor camera feeds for weapons and suspicious behavior. The system, provided by Coram AI, represents a compromise between security concerns and maintaining a welcoming educational environment - no metal detectors, but constant AI surveillance.

The school's decision reflects a broader trend toward algorithmic risk assessment in everyday settings. Rather than implementing visible security measures that might make students uncomfortable, administrators are opting for invisible AI systems that promise to identify threats before they materialize. It's a seductive proposition: enhanced safety without the appearance of a fortress.

The Oversight Problem

What makes these developments particularly concerning is how they're being implemented without adequate oversight or public debate. The New Orleans facial recognition program operated in secret, violating established protocols. Salem's AI system was chosen without extensive community input about the implications of constant surveillance in schools. These decisions are being made by individual departments and institutions, creating a patchwork of surveillance capabilities with little coordination or accountability.

The ACLU has called for investigations and permanent cessation of unauthorized surveillance programs, but the technology is advancing faster than regulatory frameworks can keep pace. There's currently no federal regulation governing AI use by local law enforcement agencies, leaving individual cities to navigate these complex issues largely on their own.

The Data Dilemma

Perhaps most troubling is how these systems are reshaping our relationship with privacy and presumption of innocence. When AI can flag someone as a potential criminal based on data patterns rather than actions, fundamental principles of justice come under strain. The technology doesn't just watch what we do; it attempts to predict what we might do, creating categories of pre-crime that exist in algorithmic assessments rather than courtrooms.

This predictive approach raises uncomfortable questions about bias, accuracy, and civil liberties. Facial recognition systems have documented problems with accuracy across different demographic groups. Predictive policing algorithms can perpetuate existing biases in criminal justice data. Yet these systems are being deployed with remarkable speed and minimal public scrutiny.

What Comes Next

The technology itself isn't inherently problematic. AI-powered crime prevention could genuinely make communities safer and help police allocate resources more effectively. The challenge lies in implementing these systems thoughtfully, with appropriate oversight and accountability mechanisms.

Some cities have banned government use of facial recognition technology, recognizing the privacy implications. Others are developing frameworks for algorithmic accountability in law enforcement. But these efforts remain scattered and incomplete, leaving many communities vulnerable to surveillance overreach.

The question isn't whether AI will continue transforming law enforcement - that transformation is already underway. The question is whether we'll develop the institutional wisdom to deploy these powerful tools responsibly, or whether we'll sleepwalk into a surveillance state that would make Philip K. Dick's dystopian visions look prescient rather than fictional.

The inmates who escaped in New Orleans were caught quickly, and that's genuinely good news. But the ease with which AI systems tracked them across an entire city should give us pause. When crime prevention becomes indistinguishable from mass surveillance, we may find that the cure has become worse than the disease.