In Hollywood, nestled between a strip mall and a recording studio where bands like the Rolling Stones have recorded, the residents of a small homeless encampment greet passers by with a friendly “Hi, hello, how are you doing?”
Some people respond in kind; others seem nervous and terse. But according to one of the most outgoing people here, Cedric — who didn’t want to give his last name — they simply hope that if their neighbors see them as friendly and nonthreatening, they won’t call the cops and have their tents removed. L.A. police and the Bureau of Sanitation have become increasingly strict about the “cleanup” of homeless encampments, even though most residents here have nowhere to move to.
Los Angeles has the second largest homeless population in the U.S. after New York, with an estimated 52,765 homeless individuals in 2018. The numbers are compiled by the Los Angeles Homeless Services Authority (LAHSA), a city agency that helps get people off the streets — and LAHSA says the number of people experiencing homelessness for the first time is increasing.
In an initiative started in January 2018, LAHSA is now sharing data from the Homeless Management Information System (HMIS) with researchers at the Center for Artificial Intelligence in Society (CAIS) at the University of Southern California. The researchers are using the data to build a system that can identify behaviors and outcomes, and allocate the type of housing with the greatest statistical chance of long-term success, while also reducing racial discrimination in the system. The project — Housing Allocation for Homeless Persons: Fairness, Transparency, and Efficiency in Algorithmic Design — brings together researchers from both the engineering and social work schools.
The project is informed by a 2018 study by two CAIS leads — engineering professor Phebe Vayanos and Eric Rice, a professor at the USC Suzanne Dworak-Peck School of Social Work — which examined the efficiency and fairness of housing allocation programs. They analyzed national data on homeless youth, aggregating…