Date of Award
Summer 8-26-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department/Program
Digital Forensics and Cybersecurity
Language
English
First Advisor or Mentor
Jennifer Holst
Second Reader
Hunter Johnson
Third Advisor
Aftab Ahmad
Abstract
Predictive policing uses machine learning to analyze crime patterns and help law enforcement better efficient use their resources. These tools can improve accuracy by highlighting complex trends in large sets of data. While this technology has its advantages, it also raises important ethical and social questions. Within this paper we looks at how predictive policing works, focusing on the machine learning models often used such as decision trees, random forests, gradient boosting, and models that factor in both time and location. It also explores how these tools might unintentionally reinforce biases already present in historical crime data. In reviewing the research, this paper highlights concerns about racial and economic disparities, transparency, and public trust. To address these concerns, it suggests incorporating fairness-focused methods, such as adversarial debiasing and fair representation. As well as using simpler models as a baseline to help assess fairness more clearly.
Recommended Citation
Lizewski, Emily, "Machine Learning and Crime Prevention" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/jj_etds/369
Included in
Artificial Intelligence and Robotics Commons, Criminology Commons, Cybersecurity Commons, Graphics and Human Computer Interfaces Commons, Social Control, Law, Crime, and Deviance Commons
