Language from police body camera footage shows racial disparities in officer respect

Abstract

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.racial disparities | natural language processing | procedural justice | traffic stops | policing

Assessing the effects of body‐worn cameras on procedural justice in the Los Angeles Police Department*

“…Moreover, in recent data coded from the Oakland (CA) Police Department's BWCs, Voigt et al. () found that Black motorists were accorded less courtesy and more discourtesy versus their White counterparts during traffic stops.…”

“…On the other hand, in work indirectly related to procedural justice, Mastrofski et al (2002) found disrespectful displays by police to be predicted by citizens' lower income status. Moreover, in recent data coded from the Oakland (CA) Police Department's BWCs, Voigt et al (2017) found that Black motorists were accorded less courtesy and more discourtesy versus their White counterparts during traffic stops.…”

Problems Establishing Identity/Residency in a City Neighborhood during a Black/White Police–Citizen Encounter: Reprising Du Bois’ Conception of Submission as “Submissive Civility”

“…The use of exaggerated speech, coupled with the term "brah," to sound Black, could be taken either as an attempt at alignment, or as mocking CR. A recent study by Voigt et al (2017) suggests that this sort of informal address by the police is used overwhelmingly with Black citizens, whereas they are much more likely to use terms like "mister" and last names with Whites. Voigt et al consider this disrespectful. As with the joke the officer opened with, CR does not treat the turn as affiliative, and refuses to align with the slang.…”

Learning to Listen: Critically Considering the Role of AI in Human Storytelling and Character Creation

“…Just as recent work in natural language processing has criticized and sought ways to rectify the amplification of negative biases (e.g. gender biases) in NLP techniques (Dwork et al, 2012;Zhao et al, 2017;Bolukbasi et al, 2016;Voigt et al, 2017), AI storytelling has the potential to amplify and exacerbate issues of bias and diversity, which in turn excludes certain individuals from experiencing the potential benefits of story engagement. For example, AI story generators that learn from existing narrative corpora may learn that straight white male characters are best suited to be protagonists or figures of power, and that genderqueer and people of color should occupy sidekick roles.…”