Amazon’s Facial “Rekognition” Mismatches 28 Lawmakers With Criminals
Above Photo: From Activistpost.com
The American Civil Liberties Union of Northern California tested Amazon’s facial Rekognition software and the program erroneously and hilariously identified 28 members of Congress as people who have been arrested for crimes.
According to Jake Snow, an ACLU attorney, the ACLU downloaded 25,000 mugshots from a “public source.”
The ACLU then ran the official photos of all 535 members of Congress through Rekognition, asking it to match them up with any of the mugshots—and it ended up mix-matching 28 members to mug shots. Ooops!
Out of those 28, the ACLU’s test flagged six members of the Congressional Black Caucus, including Rep. John Lewis (D-Georgia.)
Facial recognition historically has resulted in more false positives for African-Americans.
The test comes just two months after the Congressional Black Caucus wrote to Amazon CEO Jeff Bezos expressing concern over the “profound negative consequences” of the use of such technology.
Snow added in the blog post that the entire test costed “$12.33—less than a large pizza.”
The ACLU is rightfully concerned that faulty facial recognition scans, particularly against citizens of color, would result in a possible fatal interaction with law enforcement. Amazon’s Rekognition has already been used by a handful of law enforcement agencies nationwide.
Because of these substantive errors, Snow said the ACLU as a whole is again calling on Congress to “enact a moratorium on law enforcement’s use of facial recognition.”
Activist Post previously reported on another test of facial recognition technology in Britain which resulted in 35 false matches and 1 erroneous arrest. So the technology is demonstrated to be far from foolproof.
This comes amid a protest by Amazon employees who are against the company selling facial recognition technology to the government.
It also comes on the heels of a letter to Amazon CEO Jeff Bezos by 20 groups of Amazon shareholders urging him to stop selling the company’s face recognition software to law enforcement.
Numerous civil rights organizations have also co-signed a letter demanding Amazon stop assisting government surveillance; and several members of Congress have expressed concerns about the partnerships.
Amazon responded by essentially shrugging off the employees’ and shareholder concerns by the head of the company’s public sector cloud computing business, stating the team is “unwaveringly” committed to the U.S. government.
“We are unwaveringly in support of our law enforcement, defense and intelligence community,” Teresa Carlson, vice president of the worldwide public sector for Amazon Web Services, said July 20th at the Aspen Security Forum in Colorado, FedScoop reported.
Amazon has publicly promoted how police have used its face recognition software to identify people of interest to law enforcement. On Amazon’s website, a systems analyst with Oregon’s Washington County explained how Rekognition was fed a database of 300,000 arrest photos to match against faces seen in surveillance images. It’s significant to note that when a person is arrested they are typically put into a database, whether they are convicted of a crime or not.
In May, the ACLU released troubling internal documents, including an email from a Washington County official, telling Amazon they were using Rekognition to identify “unconscious or deceased individuals” as well as “possible witnesses.”
The privacy concerns are obvious and growing as the U.S. deploys face recognition systems in airports and at borders; and even schools are installing cameras which soon could be equipped with facial recognition.
Increasingly our rights are decreasing with the help of big corporations like Amazon. But the more worrying problem is the fact these systems are flawed and inaccurate as has been proven time and time again.
Despite this, these systems are becoming more widely used, and that is an incredibly disturbing prospect for our future if law enforcement is equipped with these facial recognition systems.