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
UChicago Crime Lab’s Policing Leadership Academy Informs First-Ever Bipartisan Federal Legislation to Expand Commander-Level Law Enforcement Training
Bipartisan Promoting Police Leadership Act, introduced by Senators Cornyn and Whitehouse, draws on UChicago model to set national standards
Second City is serious about using improv to train police leaders
Steve Hendershot for Crain’s Chicago Business joined an improv workshop hosted by The Second City for participants of our Policing Leadership Academy (PLA), where they build the skills that make great leaders: active listening, mental agility, putting aside assumptions, and communicating under pressure.
University of Chicago Economist Jens Ludwig Testifies on Violence Reduction Research Before Illinois House Committee
The Illinois House Gun Violence Prevention Committee will today hear testimony from Dr. Jens Ludwig, who will detail violence reduction strategies from his book, “Unforgiving Places: The Unexpected Origins of American Gun Violence.”