(TNS) – Artificial intelligence is making its way into law enforcement.
However, machine learning has not yet become widespread in local police departments.
“We don't do any predictive enforcement, we don't use artificial intelligence to predict where crime will occur, we don't use any predictive analytics,” said Waterloo Police Chief Joe Liebold. “I haven't.”
The agency has several AI tools at its disposal.
Liebold said the Axon body-worn cameras issued to all officers include transcription capabilities.
When police officers interact with the public or respond to calls, they turn on their cameras and record what's happening without blinking. When downloading footage from a camera, officers can use this tool to automatically generate a written description of the conversations and sounds picked up by the microphone.
Although it is designed as a time-saving device, Liebold said most police officers bypass the technology and instead transcribe by hand for greater accuracy.
Another time-saving device used by police allows investigators to quickly sort through hours of video to find relevant parts. For example, if a car is stolen over the weekend, a police officer can program the software to flag the section of the video where the action took place, instead of staring at her 48 hours of footage.
“You can skip to the point where there's movement, but it's all based on what the officer said they have to look at these videos. It's not like something is going to be done randomly,” Liebold said. Told.
The Waterloo Police Department also uses license plate readers, which are cameras mounted on patrol cars. The cameras operate in the background, passively scanning the plates of passing vehicles and checking them against law enforcement databases. When a stolen vehicle's plates are encountered, an alert is sent to a police officer, who can identify the auto collision and take appropriate action.
“We recovered several stolen cars by them. I can't say how many,” Liebold said.
Number plate data is stored for a short period of time before being disposed of, allowing authorities to confirm the existence of specific vehicles that may be of interest later.
For years, the city has installed red light and speed cameras, photographed license plates and issued citations to drivers who commit violations at certain intersections around the city. But these cameras don't collect license plates of non-violent drivers, Liebold said.
Many downtown areas also have city-operated fixed surveillance cameras. His 4th Street pedestrian bridge at Landmark also has them. The cameras have been used to solve crimes, such as the case of a man who broke into local businesses.
Although the camera is not linked to a facial recognition system, the image quality is good enough to recognize the person in the camera from a human perspective, Liebold said.
Other technologies are coming.
One such system is predictive policing. It uses statistical crime data and incident reports to make recommendations on where authorities should focus their attention.
The technology sometimes faces pushback. In 2020, the city of Santa Cruz, California, became one of the first municipalities to ban predictive policing and facial recognition, which it had used for about a decade, according to the Los Angeles Times. Critics said the technology disproportionately targets minority communities, the Times article said.
Other departments are using AI to scrutinize officers' body camera footage for hints of poor police work or inappropriate behavior. The Waterloo Police Department conducts random body camera inspections without the aid of AI, Liebold said, and selects a percentage of videos for human inspection.
“We're doing human sampling. We're having supervisors review random body camera footage to look for best practices,” he said. “Honestly, I think this is a people’s job.”
Meanwhile, cutting-edge technology is being used locally to train police officers.
Hawkeye Community College's Police Science Program recently added virtual reality simulation to its curriculum.
“Nothing is pre-recorded. 100 percent of the movement and audio is controlled by the operator,” said Ben Scholl, Hawkeye's law enforcement director.
The Apex officer system uses VR goggles and mockups of plastic handguns and Tasers to walk officer cadets through a series of scenarios, such as a traffic stop, hostage situation, or someone just having a bad day. To do.
The settings range from back alleys to apartments to offices, and feature lifelike characters controlled by instructors. When the instructor speaks, the virtual character's mouth moves. They have different facial expressions and can take out driver's licenses and guns.
Scholl said the virtual reality system is more than a shoot-or-don't-shoot simulator. Communication skills play an important role in training.
“Some will eventually come into effect, some will not. It could go either way,” he said. “This is an area where we can identify training needs and strengths.”
Scholl said the most requested training scenarios simulate mental health calls and First Amendment auditors, social media streamers who push the limits of filming their interactions with police. .
In the past, such classes were practiced on the university's MILO simulator, where characters and environments were projected onto a flat screen in front of the executives as pre-recorded scenarios were played. . Officers had limited options in training, and so did characters.
“There was no benefit to it moving on the screen. Now it starts to offset, so you can see the angle. The screen moves around as expected, so you get a more natural response,” Scholl said.
Apex Officer Simulator isn't just for Hawkeye students. The university rents the system to local law enforcement agencies for ongoing training.
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