On a typical day in the United States, police officers make more than 50,000 traffic stops. The research team from both the Stanford Computational Journalism Lab and the Stanford School of Engineering, gathered, analyzed and released records from millions of traffic stops by law enforcement agencies across the country. The project’s goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.
The team’s 2020 research paper concluded:
“We assessed racial disparities in policing in the United States by compiling and analysing a dataset detailing nearly 100 million traffic stops conducted across the country. We found that black drivers were less likely to be stopped after sunset, when a ‘veil of darkness’ masks one’s race, suggesting bias in stop decisions. Furthermore, by examining the rate at which stopped drivers were searched and the likelihood that searches turned up contraband, we found evidence that the bar for searching black and Hispanic drivers was lower than that for searching white drivers. Finally, we found that legalization of recreational marijuana reduced the number of searches of white, black and Hispanic drivers—but the bar for searching black and Hispanic drivers was still lower than that for white drivers post-legalization. Our results indicate that police stops and search decisions suffer from persistent racial bias and point to the value of policy interventions to mitigate these disparities.”
Company Type: Academia
Region: US & Canada
Industry Category: Law Enforcement
Fighting Type of Bias: Racial bias
Data set: Open Policing Project
Research: A large-scale analysis of racial disparities in police stops across the United States
Video
Location
Stanford, CA 94305
United States
Website
https://openpolicing.stanford.edu/