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The NYPD has developed new technology called Patternizr, an algorithmic machine-learning software that sifts through police data to find patterns and connect similar crimes. Developed by the New York Police Department, Patternizr is the first tool of its kind in the nation. It's been in use by NYPD since December 2016, but its existence was first disclosed by the department this month.

What started as a peculiar crime quickly turned more sinister when a syringe-wielding man caught trying to steal power drills from a Home Depot in the Bronx attacked an employee with the hypodermic needle. The NYPD quickly realized the suspect, who got away from the scene, had likely done this before. In another incident, a man caught shoplifting a drill had also waved around a hypodermic needle to threaten an employee at a Home Depot in Manhattan. The pattern was detected not by an officer combing through records, but by an analyst utilizing computer software, Patternizr, developed by the NYPD to better fight crime.

Researchers from Northwestern University and Facebook published new research in the INFORMS journal Marketing Science that sheds light on whether common approaches for online advertising measurement are as reliable and accurate as the "gold standard" of large-scale, randomized experiments.

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 New York Police Department has touted the successful use of its homegrown crime analysis tool to identify potential criminals, but while the pattern recognition tool highlights the widespread potential for advanced analytics, it also raises questions around AI bias. Patternizr, a set of machine learning models developed in-house at the NYPD, is the first crime analysis tool of its kind to be used in law enforcement. It searches through hundreds of thousands of crimes across all 77 precincts in the department's database to find patterns.