Over the last decade, police departments across the U.S. have spent millions of dollars equipping their officers with body-worn cameras that record what happens as they go about their work. Everything from traffic stops to welfare checks to responses to active shooters is now documented on video. The cameras were pitched by national and local law enforcement authorities as a tool for building public trust between police and their communities in the wake of police killings of civilians like Michael Brown, an 18 year old black teenager killed in Ferguson, Missouri in 2014.

At Washington State University’s Complex Social Interactions Lab, researchers use a combination of human reviewers and AI to analyze video. The lab began its work seven years ago, teaming up with the Pullman, Washington, police department. The lab has a team of around 50 reviewers — drawn from the university’s own students — who comb through video to track things like the race of officers and civilians, the time of day, and whether officers gave explanations for their actions, such as why they pulled someone over. The reviewers note when an officer uses force, if officers and civilians interrupt each other and whether an officer explains that the interaction is being recorded. They also note how agitated officers and civilians are at each point in the video.

Machine learning algorithms are then used to look for correlations between these features and the outcome of each police encounter.

“From that labeled data, you’re able to apply machine learning so that we’re able to get to predictions so we can start to isolate and figure out, well, when these kind of confluences of events happen, this actually minimizes the likelihood of this outcome,” said David Makin, who heads the lab and also serves on the Pullman Police Advisory Committee.

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