Machine Learning
Machine learning tools continually leverage data to “learn” and improve performance — whether that’s cleaning datasets or analyzing the data within them to make recommendations.
Good data is essential to developing successful interventions to reduce violence and reform our criminal justice system. But too often, public safety datasets are disjointed or otherwise incomplete, which makes it difficult to analyze the effects of an intervention. Machine learning tools can strengthen data analysis by gathering information across multiple datasets or by making predictions based on trends in the data — allowing researchers to analyze data more efficiently.
The Crime Lab team is utilizing and developing machine learning tools to expand and improve our data analysis capacity within many of our projects.
Latest Updates
Curbing gun violence in Chicago doesn’t require that we first end poverty
Crime Lab leaders Katie Hill and Jens Ludwig penned an op-ed in the Chicago Tribune highlighting the root causes of gun violence and the growing body of research showing that increased neighborhood vibrancy generates surprisingly large changes in the prevalence of gun violence, by as much as 30%.
A fighting chance
In The University of Chicago Magazine’s Fall ’25 issue, Susie Allen profiles Crime Lab Pritzker Director Jens Ludwig to discuss his book, “Unforgiving Places,” which challenges conventional wisdom on gun violence and suggests new approaches to solving the problem.
Researchers: Violence intervention and policing should complement, not compete against, each other
The Crime Lab’s Executive Director Katie Hill and Pritzker Director Jens Ludwig discuss evidence-based solutions to crime, including violence intervention and data-driven policing, in an op-ed for the Chicago Tribune.