SAN FRANCISCO (KGO) -- San Francisco District Attorney George Gascon will launch a program to try and prevent implicit bias in charging decisions.
It's a groundbreaking program created by Artificial Intelligence technology that will mask any reference to race in the evidence prosecutors are given.
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"As human beings, everyone of us has implicit bias. And because they're implicit, we don't necessarily recognize that we operate with those biases," Gascon said in a news briefing Wednesday.
It's biases that could influence a prosecutor's charging decision without their being aware of it. The kind of thing Gascon wants to stop. To do that he enlisted the help of Stanford University, which used Artificial Intelligence technology to create a program on a web platform, where algorithms redacted any reference to race in police incident reports seen by prosecutors.
Among the information removed, "There are officers names. There are suspect names. There are victims names. There's locations and there's direct race tokens but other things like brown hair and brown eye," explained Alex Chohlas-Wood, Deputy Director of the Stanford Computational Policy Lab.
Locations are removed because it could be a neighborhood occupied primarily by one racial or ethnic group.
Even the officer's name, which may tell prosecutors which police station he works out of-- another potentially revealing detail about the neighborhood.
Once the prosecutor makes a preliminary charging decision based on other evidence in the case, a second review begins.
"That would have the entire history including videotapes that will now ensure, provide information about a person's race," said Gascon.
It's only then that the prosecutor knows about the race of the suspect and victims. A final decision is then based on the totality of the evidence.
The program becomes operational July first-- and Stanford is doing all this for free.
Gascon hopes this program will be a model for other DA's across the country to emulate.
SF DA launching groundbreaking program to combat implicit bias in charging decisions