
‘Scan your face’ laws for the web are having unexpected consequences
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Diverging Reports Breakdown
Amazon’s Face Recognition Falsely Matched 28 Members of Congress With Mugshots
The ACLU recently conducted a test of the facial recognition tool, called “Rekognition.” The software incorrectly matched 28 members of Congress, identifying them as other people who have been arrested for a crime. The false matches were disproportionately of people of color, including six members of the Congressional Black Caucus, among them Rep. John Lewis (D-Ga.) These results demonstrate why Congress should join the ACLU in calling for a moratorium on law enforcement use of face surveillance.
Our test used Amazon Rekognition to compare images of members of Congress with a database of mugshots. The results included 28 incorrect matches. The false matches were disproportionately of people of color, including six members of the Congressional Black Caucus, among them civil rights legend Rep. John Lewis (D-Ga.). These results demonstrate why Congress should join the ACLU in calling for a moratorium on law enforcement use of face surveillance. To conduct our test, we used the exact same facial recognition system that Amazon offers to the public, which anyone could use to scan for matches between images of faces. And running the entire test cost us $12.33 — less than a large pizza. Tell Amazon to get out of the surveillance business Using Rekognition, we built a face database and search tool using 25,000 publicly available arrest photos. Then we searched that database against public photos of every current member of the House and Senate. We used the default match settings that Amazon sets for Rekognition.
Rep. Sanford Bishop (D-Ga.) was falsely identified by Amazon Rekognition as someone who had been arrested for a crime. In a recent letter to Amazon CEO Jeff Bezos, the Congressional Black Caucus expressed concern about the “profound negative unintended consequences” face surveillance could have for Black people, undocumented immigrants, and protesters. Academic research has also already shown that face recognition is less accurate for darker-skinned faces and women. Our results validate this concern: 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.