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Amazon's Facial Recognition Tool Falsely Matched 28 Members of Congress to Mug Shots

Slate

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. The ACLU released a report on Thursday revealing that Rekognition, Amazon's facial recognition tool, had falsely matched 28 members of Congress to mug shots. Members of the ACLU purchased the version of Rekognition that Amazon offers to the general public and ran public photos of every member of the House and Senate against a database of 25,000 arrest photos. The entire experiment costed $12.33, which, as ACLU attorney Jake Snow writes in a blogpost, is "less than a large pizza." Almost 40 percent of the representatives that Rekognition falsely matched were people of color, even though they make up only 20 percent of Congress.


Amazon's facial-recognition tool misidentified 28 lawmakers as people arrested for a crime, study finds

Washington Post - Technology News

Amazon.com's facial recognition tools incorrectly identified Rep. John Lewis (D-Ga.) and 27 other members of Congress as people arrested for a crime during a test commissioned by the American Civil Liberties Union of Northern California, the watchdog said Thursday. The ACLU said its findings show that Amazon's so-called Rekognition technology -- already in use at law-enforcement agencies in Oregon and Orlando -- is hampered by inaccuracies that disproportionately put people of color at risk and should prompt regulators to halt "law enforcement use of face surveillance." Amazon chief executive Jeffrey P. Bezos owns The Washington Post. For its test, the ACLU of Northern California created a database of 25,000 publicly available arrest photos, though the civil liberties watchdog did not give details about where it obtained the images or the kinds of individuals in the photos. It then used Amazon's Rekognition software to compare that database against photos of every member of the U.S. House and Senate.


Amazon face recognition falsely matches 28 lawmakers with mugshots, ACLU says

The Guardian

Amazon's facial recognition technology falsely identified 28 members of Congress as people who have been arrested for crimes, according to the American Civil Liberties Union (ACLU). The ACLU of Northern California's test of Amazon's controversial Rekognition software also found that people of color were disproportionately misidentified in a mugshot database, raising new concerns about racial bias and the potential for abuse by law enforcement. The report followed revelations in May that Amazon has been marketing and selling the Rekognition technology to police agencies, leading privacy advocates to urge CEO Jeff Bezos to stop providing the product to the government. "Our test reinforces that face surveillance is not safe for government use," Jacob Snow, a technology and civil liberties attorney at the ACLU Foundation of Northern California, said in a statement. "Face surveillance will be used to power discriminatory surveillance and policing that targets communities of color, immigrants, and activists.


Amazon's Facial Recognition System Mistakes Members of Congress for Mugshots

WIRED

Amazon touts its Rekognition facial recognition system as "simple and easy to use," encouraging customers to "detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases." And yet, in a study released Thursday by the American Civil Liberties Union, the technology managed to confuse photos of 28 members of Congress with publicly available mug shots. Given that Amazon actively markets Rekognition to law enforcement agencies across the US, that's simply not good enough. The ACLU study also illustrated the racial bias that plagues facial recognition today. "Nearly 40 percent of Rekognition's false matches in our test were of people of color, even though they make up only 20 percent of Congress," wrote ACLU attorney Jacob Snow.


Amazon's Facial Recgonition Software Has a Dangerous Race Problem

#artificialintelligence

In a report published Thursday, the American Civil Liberties Union found that Amazon's facial recognition software mistakenly matched 28 U.S. Congresspeople to photos from a mugshot database. The software--which is already in use by some police departments--was disproportionately inaccurate in identifying people of color. In the test, the ACLU used Amazon's Rekognition software to compare photos of the 535 members of the House and Senate to a database of 25,000 mugshots, for an overall inaccuracy rate of 5%. But while only 20% of the members of Congress are non-white, about 40% of the falsely ID'd legislators were men and women of color. The potential outcomes of such misidentifications in life-or-death police encounters are terrifying to consider.