The black box of justice: How secret algorithms have changed policing

The story of predictive policing begins in the 1990s with a process developed by the New York Police Department. Today New York is one of the safest big cities in America. In 2018, 289 people were murdered in the five boroughs. The city’s murder rate—3.31 per 100,000 people—was the lowest measured in 50 years

In 1990, it was a different city: 2,245 people were murdered, a rate of around 31 per 100,000 (the city’s population increased markedly in the intervening 28 years). Here’s what the New York Times said about its hometown at the end of 1990: “The streets already resemble a New Calcutta, bristling with beggars. . . . Crime, the fear of it as much as the fact, adds overtones of a New Beirut. . . . And now the tide of wealth and taxes that helped the city make these streets bearable has ebbed. . . . Safe streets are fundamental; going out on them is the simplest expression of the social contract; a city that cannot maintain its side of that contract will choke.” To stop the choking, the city knew it had to get crime under control, but the police didn’t have the right information.

In 1993, New York elected its first Republican mayor in almost 30 years—an ambitious former federal prosecutor named Rudy Giuliani. It may seem hard to believe now, but back then Rudy seemed to have at least a modicum of political nous. He ran a law-and-order campaign, and soon after taking office appointed Bill Bratton, formerly Boston’s police commissioner, then head of New York City’s Transit Police, to head the NYPD.


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