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L.A. Researchers To Use AI In Monitoring Traffic Stops

Researchers will use artificial intelligence to analyze the tone and word choice that Los Angeles Police Officers use during traffic stops, part of a broader study of whether police language sometimes unnecessarily escalates public encounters, reports the Los Angeles Times. . Findings from the study, conducted by researchers from the University of Southern California and elsewhere, will be used to help train officers on how best to navigate encounters with the public and to “promote accountability,” said Commander Marla Ciuffetelli of the Office of Constitutional Policing & Policy. Machine learning, she told the Board of Police Commissioners, “is in its infancy, but will undoubtedly become a profound element in officer training in the future.” Over three years, researchers will review body camera footage from roughly 1,000 traffic stops, then develop criteria on what constitutes an appropriate interaction based on public and office feedback and a review of the department’s policies, according to Benjamin Graham, an associate professor of international relations at USC and one of the study’s authors.


These criteria will then be fed into a machine learning program, which will “learn” how to review videos on its own and flag instances where officers cross the line, Graham said. He acknowledged that certain standards are subjective, and that researchers may not always have the same observations of a single interaction.

“Even something as simple as, did the officer introduce themselves?” he said. In analyzing the findings, researchers will consider such factors as the location of the stop and the driver’s race, as well the officer’s rank, age and experience. They will also go to “great pains” to anonymize officers and subjects, Graham said. Unlike other large police agencies like New York City’s, the LAPD does not have a dedicated unit to audit the countless hours of body camera footage gathered from police encounters every month. The department does review footage, mostly from incidents in which officers use force or after a personnel complaint has been filed.

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