Language from police body camera footage shows racial disparities in officer respect
By Rob Voigt, Nicholas P. Camp, Vinodkumar Prabhakaran, William L. Hamilton, Rebecca C. Hetey, Camilla Griffiths, David Jurgens, Dan Jurafsky, Jennifer L. Eberhardt. Proceedings of the National Academy of Sciences of the United States of America, 2017
328 citations recorded by the source corpus.
Subfields: Clinical Psychology, General Health Professions, Political Science and International Relations
View Paper: https://www.semanticscholar.org/paper/75d0cb419d7d58e81c2975758a36a11544a9f930
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Citations in the corpus
- A Framework for the Computational Linguistic Analysis of Dehumanization (2020, background): “Language is central to studying this phenomenon; like other forms of bias (Wiebe et al., 2004; Greene and Resnik, 2009; Recasens et al., 2013; Voigt et al., 2017; Breitfeller et al., 2019), dehumanizing attitudes are expressed through subtle linguistic manipulations, even in carefullyedited texts.”
- A Few Bad Apples? Racial Bias in Policing (2017, background): “This insight has also been made by research that documents racial differences in the quality of police-civilian interactions (Najdowski, 2011; Najdowski et al., 2015; Trinkner and Goff, 2016; Voigt et al., 2017).”
- A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (2019, background): “For example, in a study of the respectfulness of police language, Voigt et al. (2017) found that officers were consistently less likely to use respectful language with black community members than with white community members—a disparity in a positive social dimension.”
- Coarse race data conceals disparities in clinical risk score performance (2023, background): “…to study inequality and algorithmic bias (Chetty et al., 2020; Goel et al., 2016; Franchi et al., 2023; Rho et al., 2023; Kleinberg et al., 2018; Voigt et al., 2017; Kline et al., 2022; Laufer et al., 2022; Pierson, 2020; Liu and Garg, 2022; Derenoncourt and Montialoux, 2021; Chouldechova,…”
- Equity Beyond Bias in Language Technologies for Education (2019, background): “…algorithms are known to produce discriminatory results, with disparate outcomes by gender (Volkova et al., 2013), race (Kiritchenko and Mohammad, 2018), and age (D´ıaz et al., 2018), perpetuating a quantifiable trend of disproportionate surveillance impact for people of color (Voigt et al., 2017).”
- The Numbers Don’t Speak for Themselves: Racial Disparities and the Persistence of Inequality in the Criminal Justice System (2018, background): “We found that officers’ language was less respectful when directed at Blacks than when directed at Whites, even after controlling for factors such as the race of the officer, the severity of the infraction, the location of the stop, and the outcome of the stop (Voigt et al., 2017).”