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Will Algorithmic Tools Help or Harm the Homeless?

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A homeless encampment in Hollywood, Los Angeles.

On any given night, more than half a million people in the United States are homeless. Many who lose their housing do so only briefly, but 40 percent become chronically homeless.

Currently, many city’s social service programs operate within a “progressive engagement” model that prioritizes providing aide—things like housing subsidies, behavioral services, and job training—to the people who have been homeless the longest. There’s an obvious logic to this distribution method: People who have struggled the longest may be the most in need of help. However, the longer people are homeless, the more difficult it becomes to help them. The persistently homeless sustain $10,000 more in costs—medical and otherwise—every year than those with housing. “After all of the wreckage of being on the sidewalk for a year or three, health problems double, substance abuse goes up, and police encounters go way up,” says Daniel Flaming, the president of Economic Roundtable, a non-profit that does policy research on issues like homelessness.

But what if there was a way to identify the people most at risk for persistent homelessness and offer them aide, altering their course and preventing years of untold suffering and expenses? Economic Roundtable thinks they have developed a tool that can, according to a new report. The report introduces two statistical modeling tools that aim to pick out the adults most at risk of becoming chronically homeless after losing their jobs and the children receiving public assistance most at risk of entering long-term homelessness during their first three years as adults.

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Thanks !

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