Since revelations of a pattern of wrongdoing first came to light 23 years ago, the Oakland Police Department (OPD) has been under federal court oversight—the longest for any American city.
I’d been summoned to Oakland due to my work as a researcher focused on racial bias and inequality. The plaintiffs’ attorneys wanted to understand what was really happening between officers and civilians that might be contributing to systemic abuses and a breakdown of trust. The job for me and my team was to parse the data and try to quantify racial disparities. But we were also seeking clues about how those disparities took root, and whether anything could be done to address them.
As it turns out, as far back as 2014, Oakland was sitting on an extraordinary trove of data that contained everything we needed to know—it was just that nobody had thought of it as data before. Oakland had been an early adopter of police body-worn cameras, a rare technological innovation that consistently enjoys about 90% public approval. For over a decade now, police departments across the country have amassed troves of footage from these cameras, creating the most comprehensive record of interactions between officers and civilians in history. Yet the vast majority of that footage is never leveraged to improve those interactions.
But AI can.
Can AI improve policing?
Like a DNA sequencer or an X-ray machine, these insights let us see, for the first time ever, the actual makeup of a police encounter. We discovered linguistic signatures that could reliably predict, within the first 27 seconds of an encounter, which interactions were likely to escalate and which would conclude calmly. We were also able to map the “respect gap” that officers, including Black officers, imposed on Black drivers before the driver even spoke, finding consistent disparities in tone, word choice, whether officers explained the reason for the stop, and expressions of concern for a driver’s safety.
After implementing new policies and trainings, stops of Black civilians in Oakland dropped by 43%—without any uptick in crime. In one pre-post study of a training designed to improve relations with the public, our footage analysis revealed a marked decrease in officer language that tended to trigger escalation, and a marked increase in language that built trust.
Returning to Oakland
When I returned to Oakland not long ago, I found myself in an elevator with a fellow Black woman who worked for the police department. She told me that for years, the department would dismiss claims from community members about how they had been negatively treated, insisting that officers treat everyone professionally. “Data,” she said, “gave us a way to be heard”—a way for individual stories to become fodder for systemic change.
At a time when American policing is under enormous strain—with retirement at record highs, recruitment at record lows, officer wellness declining, and public trust in law enforcement diminished—the Oakland model delivers a ray of hope, even as it continues to battle to show the world it is capable of reform. And as more cities adopt this approach, I’m optimistic American policing could be transformed.
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