Graduation Year

2025

Date of Submission

4-2025

Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts

Department

Mathematics

Reader 1

Mark Huber

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Rights Information

© 2025 Stella Cheng

Abstract

Technological advancements in big data and algorithmic modeling have contributed to the rise of advanced predictive policing techniques. Law enforcement agencies utilize these tools to predict and prevent crime before it occurs. However, these techniques have been the subject of extensive ethical debates, particularly regarding their potential to reinforce racial biases. This thesis provides a historical overview and analysis of a prominent example of people-based predictive policing: the Chicago Police Department’s Strategic Subject List (SSL) program. The SSL program revolved around an algorithm designed to predict individuals' risk of involvement in a shooting, using indicators such as co-arrest networks, age, and the number of certain offenses, among others. The program’s multi-year implementation is evaluated through internal reports and an exploratory data analysis of crime data. The analysis focuses on crime trends across Chicago’s 22 police districts and investigates whether the SSL was effective in reducing crime and violence. The findings suggest that while citywide crime rates declined, the SSL had no significant impact on reducing crime in already high-crime districts. Additionally, the SSL disproportionately flagged young Black men under the age of 20 as “high-risk.” The thesis concludes with recommendations for increased transparency in predictive policing programs, including the release of more information about the Strategic Subject List program.

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