Data for Change: A Statistical Analysis of Police Stops, Searches, Handcuffings, and Arrests in Oakland, Calif., 2013-2014

Law enforcement agencies across the United States are facing claims that they discriminate against community members of color. Inquiries into these claims typically take one of two approaches: either attack the agency for intentional racism, or deny the presence of racial disparities altogether. Yet neither of these approaches has yielded adequate progress toward many agencies' stated mission of serving their communities with fairness and respect. Taking a different approach, the City of Oakland engaged our team of Stanford social psychologists to examine relations between the Oakland Police Department (OPD) and the Oakland community, and then to develop evidence-based remedies for any racial disparities we might find. Since May 2014, our team has undertaken five research initiatives. We describe our research methods, findings, and recommendations in Strategies for Change: Research Initiatives and Recommendations to Improve Police-Community Relations in Oakland, Calif. We provide a technical report of our main research initiative, a thorough analysis of OPD stop reports, in Data for Change: A Statistical Analysis of Police Stops, Searches, Handcuffings, and Arrests in Oakland, Calif., 2013-2014

Developing Speech Processing Pipelines for Police Accountability

Police body-worn cameras have the potential to improve accountability and transparency in policing. Yet in practice, they result in millions of hours of footage that is never reviewed. We investigate the potential of large pre-trained speech models for facilitating reviews, focusing on ASR and officer speech detection in footage from traffic stops. Our proposed pipeline includes training data alignment and filtering, fine-tuning with resource constraints, and combining officer speech detection with ASR for a fully automated approach. We find that (1) fine-tuning strongly improves ASR performance on officer speech (WER=12-13%), (2) ASR on officer speech is much more accurate than on community member speech (WER=43.55-49.07%), (3) domain-specific tasks like officer speech detection and diarization remain challenging. Our work offers practical applications for reviewing body camera footage and general guidance for adapting pre-trained speech models to noisy multi-speaker domains.Comment: Accepted to INTERSPEECH 202

Looking Deathworthy: Perceived Stereotypicality of Black Defendants Predicts Capital-Sentencing Outcomes

Researchers previously have investigated the role of race in capital sentencing, and in particular, whether the race of the defendant or victim influences the likelihood of a death sentence. In the present study, we examined whether the likelihood of being sentenced to death is influenced by the degree to which a Black defendant is perceived to have a stereotypically Black appearance. Controlling for a wide array of factors, we found that in cases involving a White victim, the more stereotypically Black a defendant is perceived to be, the more likely that person is to be sentenced to death

Looking Deathworthy: Perceived Stereotypicality of Black Defendants Predicts Capital-Sentencing Outcomes

Researchers previously have investigated the role of race in capital sentencing, and in particular, whether the race of the defendant or victim influences the likelihood of a death sentence. In the present study, we examined whether the likelihood of being sentenced to death is influenced by the degree to which a Black defendant is perceived to have a stereotypically Black appearance. Controlling for a wide array of factors, we found that in cases involving a White victim, the more stereotypically Black a defendant is perceived to be, the more likely that person is to be sentenced to death

From agents to objects: Sexist attitudes and neural responses to sexualized targets.

Abstract ■ Agency attribution is a hallmark of mind perception; thus, diminished attributions of agency may disrupt social-cognition processes typically elicited by human targets. The current studies examine the effect of perceiversʼ sexist attitudes on associations of agency with, and neural responses to, images of sexualized and clothed men and women. In Study 1, male ( but not female) participants with higher hostile sexism scores more quickly associated sexualized women with first-person action verbs ("handle") and clothed women with third-person action verbs ("handles") than the inverse, as compared to their less sexist peers. In Study 2, hostile sexism correlated negatively with activation of regions associated with mental state attribution-medial prefrontal cortex, posterior cingulate, temporal poles-but only when viewing sexualized women. Heterosexual men best recognized images of sexualized female bodies (but not faces), as compared with other targetsʼ bodies; however, neither face nor body recognition was related to hostile sexism, suggesting that the fMRI findings are not explained by more or less attention to sexualized female targets. Diminished mental state attribution is not unique to targets that people prefer to avoid, as in dehumanization of stigmatized people. The current studies demonstrate that appetitive social targets may elicit a similar response depending on perceiversʼ attitudes toward them.

White patients’ physical responses to healthcare treatments are influenced by provider race and gender

The healthcare workforce in the United States is becoming increasingly diverse, gradually shifting society away from the historical overrepresentation of White men among physicians. However, given the long-standing underrepresentation of people of color and women in the medical field, patients may still associate the concept of doctors with White men and may be physiologically less responsive to treatment administered by providers from other backgrounds. To investigate this, we varied the race and gender of the provider from which White patients received identical treatment for allergic reactions and measured patients’ improvement in response to this treatment, thus isolating how a provider’s demographic characteristics shape physical responses to healthcare. A total of 187 White patients experiencing a laboratory-induced allergic reaction interacted with a healthcare provider who applied a treatment cream and told them it would relieve their allergic reaction. Unbeknownst to the patients, the cream was inert (an unscented lotion) and interactions were completely standardized except for the provider’s race and gender. Patients were randomly assigned to interact with a provider who was a man or a woman and Asian, Black, or White. A fully blinded research assistant measured the change in the size of patients’ allergic reaction after cream administration. Results indicated that White patients showed a weaker response to the standardized treatment over time when it was administered by women or Black providers. We explore several potential explanations for these varied physiological treatment responses and discuss the implications of problematic race and gender dynamics that can endure “under the skin,” even for those who aim to be bias free

Language from Police Body Camera Footage Shows Racial Disparities in Officer Respect

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