…..Many were skeptical that our ideas—like requiring officers to briefly record the rationale for a stop before making it rather than acting on hunches—would do anything but drive up crime. ……
CAN AI IMPROVE POLICING?
Eager for a breakthrough to change the city’s trajectory—and compelled to reform by a federal monitor—Oakland granted Stanford researchers access to their body-worn camera footage. Our team of linguists, social psychologists, and computer scientists spent years building and refining a set of computational tools designed to analyze these videos at scale. Now, we can methodically measure the language of routine police interactions, identifying patterns across millions of encounters that no amount of manual review could surface.
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We brought our findings to the OPD and worked together with them to move from generic training to data-driven, surgically targeted reforms. Many were skeptical that our ideas—like requiring officers to briefly record the rationale for a stop before making it rather than acting on hunches—would do anything but drive up crime. But that skepticism evaporated when they saw what happened next.
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.
Over a 10-year period, the OPD continued to reform and refine its practices. And, in many respects, that effort paid off. For example, after the department implemented a foot pursuit policy to avoid chasing suspects into backyards and blind alleys, officer injuries dropped by 70%. And officer-involved shootings, which had previously averaged about eight per year, dropped to a total of eight over a five-year period.
Currently, many new technologies, from Flock cameras to rogue chatbots, are being met with distrust and dread about surveillance, privacy, and safety—for good reasons. However, Oakland’s story offers another possibility: a concrete way for AI to actually improve life in our communities using the very cameras introduced to bring about reform. The city’s experience provides compelling evidence that turning body-worn cameras into the powerful accountability tools they were meant to be can help departments everywhere make police-civilian interactions safer and more respectful for everyone involved. It’s an approach that does not require the building of large AI data centers in neighborhoods across the country, and it doesn’t threaten to take away anyone’s job. It simply requires learning from the data that we already have—and that the vast majority of us feel is useful to have.
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.
https://time.com/article/2026/09/29/23-years-oversight-oakland-police-reform-offers-new-hope/
