Together, Eberhardt, Jurafsky and seven other colleagues examined transcripts from 183 hours of body camera footage from 981 stops, which 245 different OPD officers conducted in April 2014. Besides, their own biases could affect their judgments of the interactions.” New methodology for analyzing footage Yet “researchers can’t just sit and watch every single stop,” Eberhardt explained. To satisfy demands for both privacy and transparency, the researchers needed a way to approach the footage as data showing general patterns, rather than as evidence revealing wrongdoing in any single stop. “At the same time, many departments want their actions to be transparent to the public.” “The police are already wary of footage being used against them,” Eberhardt said. Just “cherry-picking” negative or positive episodes, for example, can lead to inaccurate impressions of police-community relations overall, she said. But drawing accurate conclusions from hundreds of hours of footage is challenging, Eberhardt said. OPD, like many police departments nationwide, has been using body-worn cameras to monitor police-community interactions. In 2014, the City of Oakland contracted with Eberhardt and her team to assist the Oakland Police Department in complying with a federal order to collect and analyze data from traffic and pedestrian stops by race. The study is not the first time Eberhardt has collaborated with the OPD to study possible racial disparities in policing. “Police departments can use these tools not only to diagnose problems in police-community relations but also to develop solutions.” Video footage as data, not evidence “The fact that we now have the technology and methods to show these patterns is a huge advance for behavioral science, computer science and the policing industry,” said Rob Voigt, a Stanford linguistics doctoral student and lead author of the study. But the many small differences in how they spoke with community members added up to pervasive racial disparities.” “To be clear: There was no swearing,” said Dan Jurafsky, a study co-author and Stanford professor of linguistics and of computer science. The researchers’ novel technique demonstrated that white residents were 57 percent more likely than black residents to hear a police officer say the most respectful utterances, such as apologies and expressions of gratitude like “thank you.” Meanwhile, black community members were 61 percent more likely than white residents to hear an officer say the least respectful utterances, such as informal titles like “dude” and “bro” and commands like “hands on the wheel.” They then applied this technique to the transcripts from 981 traffic stops the Oakland Police Department (OPD) made in a single month. To analyze the body camera footage, a multidisciplinary team from Stanford’s psychology, linguistics and computer science departments first developed a new artificial intelligence technique for measuring levels of respect in officers’ language. The racial disparities in respectful speech remained even after the researchers controlled for the race of the officer, the severity of the infraction, and the location and outcome of the stop. “Our findings highlight that, on the whole, police interactions with black community members are more fraught than their interactions with white community members,” explained Jennifer Eberhardt, co-author of the study and professor of psychology at Stanford. (Image credit: Ryan Johnson/Flickr/Creative Commons)Īlthough they are subtle, these widespread racial disparities in officers’ language use may erode police-community relations, said the researchers who conducted the study, published June 5 in Proceedings of the National Academy of Sciences. Stanford researchers have developed a computational tool to analyze language extracted from police body camera footage as data for understanding law enforcement interaction with the community.