Amazon’s Facial Recognition Wrongly Identifies 28 Lawmakers, A.C.L.U. Says 

Shot:

The errors emerged as part of a larger test in which the civil liberties group used Amazon’s facial software to compare the photos of all federal lawmakers against a database of 25,000 publicly available mug shots. In the test, the Amazon technology incorrectly matched 28 members of Congress with people who had been arrested, amounting to a 5 percent error rate among legislators.

Chaser:

The test disproportionally misidentified African-American and Latino members of Congress as the people in mug shots.

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