Contributed by Jennifer L. Eberhardt, March 26, 2017 (sent for review February 14, 2017; reviewed by James Pennebaker and Tom Tyler)
Police officers speak significantly less respectfully to black than to white community members in everyday traffic stops, even after controlling for officer race, infraction severity, stop location, and stop outcome. This paper presents a systematic analysis of officer body-worn camera footage, using computational linguistic techniques to automatically measure the respect level that officers display to community members. This work demonstrates that body camera footage can be used as a rich source of data rather than merely archival evidence, and paves the way for developing powerful language-based tools for studying and potentially improving police–community relations.
Using footage from body-worn cameras, we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops. We develop computational linguistic methods that extract levels of respect automatically from transcripts, informed by a thin-slicing study of participant ratings of officer utterances. We find that officers speak 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 the building of police–community trust.
Author contributions: R.V., N.P.C., D. Jurafsky, and J.L.E. designed research; R.V. and N.P.C. performed research; V.P., W.L.H., R.C.H., C.M.G., and D. Jurgens contributed new reagents/analytic tools; R.V. and N.P.C. analyzed data; R.V., N.P.C., D. Jurafsky, and J.L.E. wrote the paper; and D. Jurafsky and J.L.E. served as PI on this project.
Reviewers: J.P., University of Texas at Austin; and T.T., Yale Law School.
Conflict of interest statement: J.L.E. was invited by a federal judge and monitor to serve as a Subject Matter Expert to assist with the Oakland Police Department’s reform efforts. The assignment began prior to the studies reported here.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1702413114/-/DCSupplemental.
Freely available online through the PNAS open access option.
http://www.pnas.org/site/misc/userlicense.xhtmlContributed by Jennifer L. Eberhardt, March 26, 2017 (sent for review February 14, 2017; reviewed by James Pennebaker and Tom Tyler)
Police officers speak significantly less respectfully to black than to white community members in everyday traffic stops, even after controlling for officer race, infraction severity, stop location, and stop outcome. This paper presents a systematic analysis of officer body-worn camera footage, using computational linguistic techniques to automatically measure the respect level that officers display to community members. This work demonstrates that body camera footage can be used as a rich source of data rather than merely archival evidence, and paves the way for developing powerful language-based tools for studying and potentially improving police–community relations.
Using footage from body-worn cameras, we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops. We develop computational linguistic methods that extract levels of respect automatically from transcripts, informed by a thin-slicing study of participant ratings of officer utterances. We find that officers speak 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 the building of police–community trust.
Author contributions: R.V., N.P.C., D. Jurafsky, and J.L.E. designed research; R.V. and N.P.C. performed research; V.P., W.L.H., R.C.H., C.M.G., and D. Jurgens contributed new reagents/analytic tools; R.V. and N.P.C. analyzed data; R.V., N.P.C., D. Jurafsky, and J.L.E. wrote the paper; and D. Jurafsky and J.L.E. served as PI on this project.
Reviewers: J.P., University of Texas at Austin; and T.T., Yale Law School.
Conflict of interest statement: J.L.E. was invited by a federal judge and monitor to serve as a Subject Matter Expert to assist with the Oakland Police Department’s reform efforts. The assignment began prior to the studies reported here.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1702413114/-/DCSupplemental.
Freely available online through the PNAS open access option.
http://www.pnas.org/site/misc/userlicense.xhtml
