STUDY: Facial-Recognition Software Has More Significant Impact On Black People
Imagine how much more difficult it would be for Jim Caviezel and Taraji P. Henson to save people’s lives in Person of Interest if the system they used was prone to racial profiling. They’d have an even harder time trying to figure out if they’re helping a potential victim or tracking a potential criminal. Luckily, that’s just a television show. Or is it..?
A Georgetown University think tank and members of the American Civil Liberties Union (ACLU) have announced their results after studying the facial-recognition software that is often used by law enforcement officials across the country – without any major checks and balances.
While immediate concerns arose concerning the accuracy of the system in comparison to how often it’s used by authorities, it was also found that its use will disproportionately affect people of color.
The Georgetown Law Center on Privacy & Technology released their findings on Tuesday in a document entitled “The Perpetual Line-Up: Unregulated Police Face Recognition in America.” It was found that 117 million Americans are affected by the facial-recognition software with 1 in ever 2 already being in the system.
According to the study, the facial-recognition software is 5 to 10 percent less accurate when it comes to properly identifying African Americans than it is for white people. This is a large concern because it could lead to the unlawful arrests of many innocent people of color or much worse.
So, not only are people of color being racially profiled in the streets by law enforcement officers, self-proclaimed neighborhood watch patrolmen and citizens, now they’re being improperly targeted by facial-recognition software that’s being used far too liberally in the first place.
The ACLU has taken this information and sent a letter to the Justice Department’s Civil Rights Division requesting an investigation into whether or not their theory on the facial-recongition technology impacting communities of color is accurate.
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