--- description: Find out why Jennifer Eberhardt is on TIME’s 2026 Closers list. title: Jennifer Eberhardt Is Analyzing Police Bias With AI image: https://static.time.com/v3/assets/bltea6093859af6183b/bltcaa080ddc5b13ac5/6998ccfa1500fbaf88aa6ed1/time-closers-2026-jennifer-eberhardt-01.jpg?branch=production&width=3840&quality=75&auto=webp&crop=16:9 --- Jan 27, 2026 # Jennifer Eberhardt Is Analyzing Police Bias With AI by [Belinda Luscombe](/author/belinda-luscombe/) ## Belinda Luscombe Update your preferences in [Account Settings](/account/preferences/) Close Editor at Large  Nana Kofi Nti To most people, footage from a police body cam is only useful as evidence. To [Jennifer Eberhardt](https://time.com/5849172/police-racial-bias/ "undefined"), it’s a rich source for research, full of data that can help explain—and maybe even level—the disparities in the treatment of Black people and others within the criminal-justice system. Eberhardt,a professor of both psychology and organizational behavior at Stanford University and a recipient of a 2014 MacArthur “genius grant,” studies bias and how a cultural association between Black Americans and crime can affect not just what people think, but also what they notice and remember, and how they punish. Eberhardt’s most famous experiments found novel ways to uncover unconscious bias. She had people think about either crime or something else before being shown an image of Black person and a white person. No matter the race of the participant, those who thought about crime looked at the image of the Black person first. People were also able to identify blurry images of guns or knives sooner after exposure to Black faces than after white faces or images without faces. “What I've been doing with a number of different colleagues across the past 25 years,” she says, “is demonstrating the power of this particular association between Blackness and crime.” [Recently](https://academic.oup.com/pnasnexus/article/3/9/pgae359/7756556 "undefined"), Eberhardt, 60, has been using AI to analyze hundreds of hours of police body-cam footage for language patterns that recur when a routine traffic stop of a Black driver for an infraction like an unbuckled seat belt or failure to signal turns into something more serious. “We found that there was a linguistic signature to these escalated stops, and that signature had two elements,” Eberhardt says. “The officer started the stop with an order, and the officer did not give the reason for the stop in those initial moments.” When interactions began in that way, it was more likely that a driver would end up handcuffed, searched, or arrested. This research, combined with earlier findings that when white drivers were pulled over, the officers usually first expressed concern for the welfare of the driver and explained the reason for the stop, suggest that small adjustments in police behavior might make a big difference. There are more than 9 million stops of this type every year; it’s one of the most common interactions the public has with police. SPARQ (Social Psychological Answers to Real-World Questions), the research unit at Stanford that Eberhardt co-leads, has worked closely with the Oakland Police Department to help develop training modules and then to assess their impact. “There are a lot of trainings out there that departments are already deploying,” says Eberhardt, who has also done projects with the San Francisco and New York Police Departments, “but we just know so little information about effectiveness, and effectiveness over time.” The effectiveness of policy is another area she’s seeking to test. In January 2024, California enacted a law that made it mandatory for police to explain the reason a car had been stopped. Eberhardt is probing how that policy is working. “With an analysis of body-worn-camera footage, we could now do this at scale in a way that we couldn't before,” she says. “We're not focused on individual officers, we're focused on broad, systematic patterns. We feel like we're able to offer information to police departments and community members around the country about what's happening generally, not one by one.” More than two decades ago, as a young professor, Eberhardt called a sheriff in nearby San Mateo County after a series of news stories suggested that his officers had been engaging in racial profiling. He invited her to his office, and after talking for a while, Eberhardt asked why he had agreed to meet with her. “He said to me that he really wanted to understand what was happening. And if his deputies were profiling, he wanted to know and to know how to address that,” says Eberhardt. But the only way he could find this information was adversarial. She realized that high-quality, neutral research about what was happening during police and public interactions could be extremely valuable in developing methods that would allow police departments to improve. She’s even more convinced this is true now, because there is so much data available and AI offers a much easier way to assess it. “I feel even more strongly that there are possibilities through data analysis to really understand police-community interactions in a way we've never understood before,” she says. In her experience, the police and the communities they serve often want the same thing, a relationship of trust and respect. “I believe that the kind of tools we’re building give us the best chance we’ve had in a long time to start repairing those relationships—making real, effective change—and bringing us closer to the outcome that we all want to see.” Jennifer Eberhardt Is Analyzing Police Bias With AI ```json [{"@context":"https://schema.org","@type":"Article","@id":"https://time.com/collections/closers-2026/7353460/jennifer-eberhardt/","mainEntityOfPage":{"@type":"WebPage","@id":"https://time.com/collections/closers-2026/7353460/jennifer-eberhardt/"},"headline":"Jennifer Eberhardt Is Analyzing Police Bias With AI","datePublished":"2026-01-27T13:02:55.000Z","dateModified":"2026-03-03T01:04:21.859Z","description":"Find out why Jennifer Eberhardt is on TIME’s 2026 Closers list.","url":"https://time.com/collections/closers-2026/7353460/jennifer-eberhardt/","keywords":["Closers 2026","list","franchise","Special Project","sponsorshipblock"],"thumbnailUrl":"https://static.time.com/v3/assets/bltea6093859af6183b/bltcaa080ddc5b13ac5/6998ccfa1500fbaf88aa6ed1/time-closers-2026-jennifer-eberhardt-01.jpg?branch=production&width=1200&quality=75&auto=webp&crop=1200:675&height=675","author":[{"@type":"Person","name":"Belinda Luscombe","jobTitle":"Editor at Large","url":"https://time.com/author/belinda-luscombe/"}],"creator":"Belinda Luscombe","articleSection":"U.S.","image":[{"@type":"ImageObject","url":"https://static.time.com/v3/assets/bltea6093859af6183b/bltcaa080ddc5b13ac5/6998ccfa1500fbaf88aa6ed1/time-closers-2026-jennifer-eberhardt-01.jpg?branch=production&width=1200&quality=75&auto=webp&crop=1200:675&height=675","width":1200,"height":675}],"publisher":{"@type":"Organization","name":"Time","url":"https://time.com/","logo":{"@type":"ImageObject","url":"https://time.com/images/logo.png","width":528,"height":156},"foundingDate":"March 3, 1923","sameAs":["https://www.facebook.com/time","https://www.instagram.com/time/?hl=en","https://twitter.com/time","https://www.pinterest.com/timemagazine"]}},{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"/section/us/","name":"U.S."}},{"@type":"ListItem","position":2,"item":{"@id":"/tag/closers-2026/","name":"Closers 2026"}},{"@type":"ListItem","position":3,"item":{"@id":"https://time.com/collections/closers-2026/7353460/jennifer-eberhardt/","name":"Jennifer Eberhardt Is Analyzing Police Bias With AI"}}]}] ```