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
How Treating Teens’ Trauma Is Stopping Violence in Chicago
The Tradeoffs Podcast highlights the Crime Lab’s study of Choose to Change, a program that pairs cognitive behavioral therapy with wraparound supports to engage young people who are increasingly disconnected from school and often exposed to high levels of trauma – with the goal of keeping them safe and helping them thrive.
Major Public Safety Associations Participate in Congressional Briefing on Law Enforcement Training Priorities During National Police Week
Alumni of the Crime Lab’s Policing Leadership Academy (PLA) participated in a bipartisan briefing as part of National Police Week, focusing on key law enforcement training priorities.
Book Review: What We Get Wrong About Violent Crime
Malcolm Gladwell pens a review of “Unforgiving Places,” a new book by Crime Lab Pritzker Director Jens Ludwig, that reflects on how the book “challenges our assumptions about why most shootings happen—and what really makes a city safe.”
