Blue Words Matter: Using Body-Worn Camera Data to Examine How Officers Speak to Black and White Drivers During Routine Traffic Stops

• In Event: Measuring Bias and Procedural Justice during Police-Community Encounters (Organized by the Division of Policing)

Abstract

By best estimates, more than one quarter of the public (ages 16 and over) comes into contact with the police during the course of a year, most frequently as the result of a police-initiated traffic stop. Such stops are not only common, they are consequential, each an opportunity to build or erode public trust in the police. Using footage from body-worn cameras, we analyzed the respectfulness of police officer language toward white and black community members during routine traffic stops in Oakland, California. We developed computational linguistic methods that extract levels of respect automatically from transcripts, informed by a thin-slicing study of participant ratings of officer utterances. Our dataset consisted of 981 traffic stops involving 245 officers. Although the officers were professional overall, we found that they spoke with consistently less respect toward black versus white community members, even after controlling for the race of the officer, the severity of the infraction, the location of the stop, and the outcome of the stop. Such disparities in common, everyday interactions between police and the communities they serve have important implications for procedural justice and police–community relations.

Authors

• Jennifer L. Eberhardt, Stanford University

• Dan Jurafsky, Stanford University

• Rob Voigt, Stanford University

• Nicholas Camp, Stanford University

• Camilla Griffiths, Stanford University