Sgt. Charles Coleman popped out of his police SUV and scanned a trash-strewn street popular with the city’s homeless, responding to a crime that hadn’t yet happened.
It wasn’t a 911 call that brought the Los Angeles Police Department officer to this spot, but a whirring computer crunching years of crime data to arrive at a prediction: An auto theft or burglary would probably occur near here on this particular morning.
Hoping to head it off, Coleman inspected a line of ramshackle RVs used for shelter by the homeless, roused a man sleeping in a pickup truck and tapped on the side of a shack made of plywood and tarps.
“How things going, sweetheart?” he asked a woman who ambled out. Coleman listened sympathetically as she described how she was nearly raped at knife point months earlier, saying the area was “really tough” for a woman.
Soon, Coleman was back in his SUV on his way to fight the next pre-crime. Dozens of other LAPD officers were doing the same at other spots, guided by the crime prognostication system known as PredPol.
“Predictive policing” represents a paradigm shift that is sweeping police departments across the country. Law enforcement agencies are increasingly trying to forecast where and when crime will occur, or who might be a perpetrator or a victim, using software that relies on algorithms, the same math Amazon uses to recommend books.
“The hope is the holy grail of law enforcement — preventing crime before it happens,” said Andrew G. Ferguson, a University of District of Columbia law professor preparing a book on big data and policing.
Now used by 20 of the nation’s 50 largest police forces by one count, the technologies are at the center of an increasingly heated debate about their effectiveness, potential impact on poor and minority communities, and implications for civil liberties.
Some police departments have hailed PredPol and other systems as instrumental in reducing crime, focusing scarce resources on trouble spots and individuals and replacing officers’ hunches and potential biases with hard data.
But privacy and racial justice groups say there is little evidence the technologies work and note the formulas powering the systems are largely a secret. They are concerned the practice could unfairly concentrate enforcement in communities of color by relying on racially skewed policing data. And they worry that officers who expect a theft or burglary is about to happen may be more likely to treat the people they encounter as potential criminals.
The experiments are one of the most consequential tests of algorithms that are increasingly powerful forces in our lives, determining credit scores, measuring job performance and flagging children that might be abused. The White House has been studying how to balance the benefits and risks they pose.
“The technical capabilities of big data have reached a level of sophistication and pervasiveness that demands consideration of how best to balance the opportunities afforded by big data against the social and ethical questions these technologies raise,” the White House wrote in a recent report.
A seismic shift in policing
It was 6:45 a.m. on a Monday, but the sheet of paper Coleman held in his hands offered a glimpse of how Oct. 24 might go: an auto theft near the corner of Van Nuys and Glenoaks, a burglary at Laurel Canyon and Roscoe and so on.
The crime forecast is produced by PredPol at the beginning of each shift. Red boxes spread across Google maps of the San Fernando Valley, highlighting 500-by-500-square-foot locations where PredPol concluded property crimes were likely.