A new computer model uses publicly available data to predict crime accurately in eight cities in the U.S., while revealing increased police response in wealthy neighborhoods at the expense of less advantaged areas.
Advances in artificial intelligence and machine learning have sparked interest from governments that would like to use these tools for predictive policing to deter crime. However, early efforts at crime prediction have been controversial, because they do not account for systemic biases in police enforcement and its complex relationship with crime and society.
University of Chicago data and social scientists have developed a new algorithm that forecasts crime by learning patterns in time and geographic locations from public data on violent and property crimes. It has demonstrated success at predicting future crimes one week in advance with approximately 90% accuracy.
In a separate model, the team of researchers also studied the police response to crime by analyzing the number of arrests following incidents and comparing those rates among neighborhoods with different socioeconomic status. They saw that crime in wealthier areas resulted in more arrests, while arrests in disadvantaged neighborhoods dropped. Crime in poor neighborhoods didn’t lead to more arrests, however, suggesting bias in police response and enforcement.
“What we’re seeing is that when you stress the system, it requires more resources to arrest more people in response to crime in a wealthy area and draws police resources away from lower socioeconomic status areas,” said Ishanu Chattopadhyay, PhD, Assistant Professor of Medicine at UChicago and senior author of the new study, which was published on June 30, 2022, in the journal Nature Human Behaviour.