The New York Police Department (NYPD) recently unveiled a new digital tool that it says can sift through police reports to help officers spot patterns of crimes potentially committed by the same criminals.

The NYPD says the program, dubbed Patternizr, can save time and help its investigators detect patterns in burglaries, robberies, and grand larcenies they might not have noticed by manually combing through the tens of thousands of reports the department receives every year. While the NYPD employs civilian crime analysts who focus on such tasks, it’s still a laborious task, and it can be hard for analysts to familiarize themselves with reports produced in different precincts from the ones where they work.

“Traditionally this is something that’s been somewhat difficult for us at the department, because crime analysts are concentrated on the precinct to which they’re assigned,” says Evan Levine, the NYPD’s assistant commissioner for data analytics and one of the authors of a paper on Patternizr recently published in the Informs Journal on Applied Analytics. “Obviously criminals don’t pay attention to precinct boundaries.”

The NYPD emphasizes that the tool doesn’t look at suspects’ race or gender in matching crimes, and that its tests have found the program no more likely to suggest links to crimes committed by people of certain races than random sampling from police reports. But civil liberties advocates still cautioned that Patternizr and other tools like it could exacerbate existing biases depending on how they’re used.

“The institution of policing in America is systemically biased against communities of color,” said New York Civil Liberties Union legal director Christopher Dunn in a statement shared with Fast Company. “Any predictive policing platform runs the risks of perpetuating disparities because of the over-policing of communities of color that will inform their inputs. To ensure fairness, the NYPD should be transparent about the technologies it deploys and allows independent researchers to audit these systems before they are tested on New Yorkers.”

Cracking the case of the needle-wielding shoplifter

Patternizr is currently being used by analysts on about 600 crimes per week, Levine says, looking for similarities between particular incidents and previously reported crime based on attributes like their distances from one another, the times of day they took place, suspects’ height and weight, and unstructured text descriptions of what happened. It was inspired by earlier research at the Massachusetts Institute of Technology, where scientists tested a version of such a tool with the Cambridge Police Department.

“What they did in New York is what we dreamed of in Cambridge,” says Cynthia Rudin, who worked on the MIT project and is now an associate professor of computer science, electrical and computer engineering at Duke University, where she runs the Prediction Analysis Lab. “NYPD had a whole data science team, whereas we just had a professor and a graduate student, so we couldn’t launch it at the scale that they launched it.”