06/30/2023
We are very happy to announce that a product of over two years of collaboration between SMART researchers, the Ann Arbor Police Department, and Ann Arbor's Independent Community Police Oversight Commission (ICPOC) is available for public consumption.
In February 2021 SMART was approached to conduct an independent analysis of AAPD traffic stops, to look especially for evidence of disparities in the frequency, type, and outcomes of those stops. The project ultimately involved dozens of hours of work planning, education, and orientation that enabled shared learning and recognition of mutual goals between each partner. It is the kind of work made possible through sustained partnerships between public universities with regional commitments and those municipal and community organizations willing to trust and work with them. The result of this particularly extensive and sustained collaboration is the most robust and nuanced analysis of disparities in traffic stops in the history of the City of Ann Arbor. It is the first such analysis to incorporate several years of traffic stop data, which in turn enabled a more nuanced and specific analysis of existing disparities. Most specifically, this analysis is the first of its kind for the City of Ann Arbor that was able to cross-tabulate race and gender in order to highlight the specific dynamics of those dimensions. It is also the first such analysis that was able to offer a more nuanced lens onto disparities in particular types of stops and post-stop outcomes, additional dimensions that became essential for our overall conclusions. We are especially appreciative of the Community Foundation for Southeast Michigan's Community Policing Innovations Initiative, which provided partial support at a crucial stage of the project.
The results of our analysis identified significant disparities across every dimension examined, with non-white motorists being Stopped and Searched more frequently and White motorists being Stopped and Searched less frequently than would be expected in every instance. These disparities were not uniform across racial categories nor across various Reasons for Contact. The largest disparities identified in this analysis involve Multi-Racial and African-American male drivers for stops initiated for Equipment Violations (which occurred 2.41x more likely than would be expected) as well as for Searches after the initial stop (which occurred between 5.4x to 3.65x more often than would be expected).
We invite you to read the full report, which is now available at
Project Overview