News Room

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.

In a new editorial, Sheldon Jacobson, INFORMS member and professor at the University of Illinois at Urbana-Champaign, discusses how the sophisticated mathematical modeling that is used to increase the odds of having a perfect – or even good – basketball bracket is the same technique used to solve the most complex challenges in both industry and government. 

When a syringe-wielding drill thief tried sticking up a Home Depot near Yankee Stadium, police figured out quickly that the crime seemed familiar, and matched it to an identical crime that occurred miles away in Manhattan. The match, though, wasn’t made by an officer looking through files. It was done using Patternizr, pattern-recognition computer software developed by the New York Police Department that allows crime analysts stationed in each of the department’s 77 precincts to compare robberies, larcenies and thefts to hundreds of thousands of crimes logged in the NYPD’s database, transforming their hunt for crime patterns with the click of a button.

A comprehensive new research study from the INFORMS journal Marketing Science identifies challenges for current measurement practices for online advertising, while calling for better ways to address so-called “unobservable” factors. The key takeaway, according to its authors, is that current “observational” approaches to digital advertising measurement may not be as effective as large-scale randomized control trials.

New research in the INFORMS journal Marketing Science has concluded that the most common methods of online advertising measurement used by advertisers and agencies may not be as accurate as the kind of “large-scale, randomized experiments” that can only be conducted via - pause for effect - walled garden platforms like Facebook.