In an attempt to demonstrate the perceived flaws inherent in facial recognition technology, the American Civil Liberties Union (ACLU) measured images of U.S. Congress members against a database of public mug shots using Amazon’s facial recognition tool, Rekognition.

The result, according to the ACLU, is that 28 members of U.S. Congress were falsely identified as criminal suspects, a finding that Amazon has refuted.

According to the retail giant, the ACLU used a lower default accuracy setting on the Amazon system (80 percent) versus the standard accuracy setting (95 percent).

Instead, the ACLU claims that the results merely highlight the failings of facial recognition technology.

"Our test reinforces that face surveillance is not safe for government use," said Jacob Snow, ACLU's technology and civil liberties lawyer.

"Face surveillance will be used to power discriminatory surveillance and policing that targets communities of colour, immigrants and activists. Once unleashed, that damage can't be undone," continued Snow.

In response to the test, a spokesperson for Amazon Web Services stated: "We remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

"With regard to this recent test of Amazon Rekognition by the ACLU, we think that the results could probably be improved by following best practices around setting the confidence thresholds."

Still, the number of people incorrectly identified, almost 40 percent of whom were African American, prompted the Congressional Black Caucus to express concern about the "profound negative unintended consequences" facial recognition systems could have for African-American people.

"Congress should press for a federal moratorium on the use of face surveillance until its harms, particularly to vulnerable communities, are fully considered," said ACLU's legislative counsel Neema Singh Guliani.

"The public deserves a full debate about how and if face surveillance should be used."

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