Amazon’s Face Recognition Falsely Matched 28 Members of Congress With Mugshots

Amazon’s face surveillance technology is the target of growing opposition nationwide, and today, there are 28 more causes for concern. In a test the ACLU recently conducted 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 members of Congress who were falsely matched with the mugshot database we used in the test include Republicans and Democrats, men and women, and legislators of all ages, from all across the country.

Amazon Rekognition False Matches of 28 member of Congress
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.

The Rekognition Scan, Comparing input images to mugshot databases
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.

Racial Bias in Amazon Face Recognition
People of color were disproportionately falsely matched in our test.

If law enforcement is using Amazon Rekognition, it’s not hard to imagine a police officer getting a “match” indicating that a person has a previous concealed-weapon arrest, biasing the officer before an encounter even begins. Or an individual getting a knock on the door from law enforcement, and being questioned or having their home searched, based on a false identification.

An identification — whether accurate or not — could cost people their freedom or even their lives. People of color are already disproportionately harmed by police practices, and it’s easy to see how Rekognition could exacerbate that. A recent incident in San Francisco provides a disturbing illustration of that risk. Police stopped a car, handcuffed an elderly Black woman and forced her to kneel at gunpoint — all because an automatic license plate reader improperly identified her car as a stolen vehicle.

Matching people against arrest photos is not a hypothetical exercise. Amazon is aggressively marketing its face surveillance technology to police, boasting that its service can identify up to 100 faces in a single image, track people in real time through surveillance cameras, and scan footage from body cameras. A sheriff’s department in Oregon has already started using Amazon Rekognition to compare people’s faces against a mugshot database, without any public debate.

Face surveillance also threatens to chill First Amendment-protected activity like engaging in protest or practicing religion, and it can be used to subject immigrants to further abuse from the government.

These dangers are why Amazon employees, shareholders, a coalition of nearly 70 civil rights groups, over 400 members of the academic community, and more than 150,000 members of the public have already spoken up to demand that Amazon stop providing face surveillance to the government.

Congress must take these threats seriously, hit the brakes, and enact a moratorium on law enforcement use of face recognition. This technology shouldn’t be used until the harms are fully considered and all necessary steps are taken to prevent them from harming vulnerable communities.

List of Members of Congress Falsely Matched With Arrest Photos


  • John Isakson (R-Georgia)
  • Edward Markey (D-Massachusetts)
  • Pat Roberts (R-Kansas)


  • Sanford Bishop (D-Georgia)
  • George Butterfield (D-North Carolina)
  • Lacy Clay (D-Missouri)
  • Mark DeSaulnier (D-California)
  • Adriano Espaillat (D-New York)
  • Ruben Gallego (D-Arizona)
  • Thomas Garrett (R-Virginia)
  • Greg Gianforte (R-Montana)
  • Jimmy Gomez (D-California)
  • Raúl Grijalva (D-Arizona)
  • Luis Gutiérrez (D-Illinois)
  • Steve Knight (R-California)
  • Leonard Lance (R-New Jersey)
  • John Lewis (D-Georgia)
  • Frank LoBiondo (R-New Jersey)
  • David Loebsack (D-Iowa)
  • David McKinley (R-West Virginia)
  • John Moolenaar (R-Michigan)
  • Tom Reed (R-New York)
  • Bobby Rush (D-Illinois)
  • Norma Torres (D-California)
  • Marc Veasey (D-Texas)
  • Brad Wenstrup (R-Ohio)
  • Steve Womack (R-Arkansas)
  • Lee Zeldin (R-New York)
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Please read the article. They did share. It's Rekognition, the database created by Amazon that the government purchased with intent to use on citizens.


While reading this article, I can only ask how did you come up with your data? You stat "The false matches were disproportionately of people of color", however, the numbers do not back up the 28 pictures you have posted. Of the 28 people wrongly identified, 6 are from the Black Caucus, 6/28= 21%. However when you look at all of the member of Congress and the Senate, 535 people, 6/535=1.1% mismatch for the men of color. If people of color make up 20% of lawmakers in Congress to have a 1.1% mismatch is not a horrible statistic. On the overall, the 5.2% mismatch for the 28 people mis-identified, 28/535, is not much off of any survey computed of a +/- %. I would be interested to see what the mismatch rate is, Amazon tells police agencies to use of 95% instead of the 80% used by the ACLU.




"This technology shouldn’t be used until the harms are fully considered and all necessary steps are taken to prevent them from harming vulnerable communities." Can you explain what kind of steps would enable you to support the use of facial recognition by law enforcement? Thank you.


I agree. I feel that most of the other comments miss the fundamental point of this article, which is that law enforcement should not be using this technology without oversight. The nature of the oversight must be determined through public debate and our political process. The technology is too powerful and flawed--and the risks to liberty are too high--to allow this technology to be marketed as a common of the shelf product.


Clearly Amazon is racist and must be prosecuted.


Dear Amazon : I now cancel cease and will no longer do any business with your company , stop this madness before you get some innocent person killed in the rush to judgement " HURRY ! SHOOT ! KILL ! ID ! REMORSE ! " WONT BRING ANYONE BACK FROM THE DEAD !!!


Pretty sure Greg Gianforte (R-Montana) does indeed have a mugshot for assaulting reporter Ben Jacobs for no reason


As the quote goes, "There are three kinds of lies: lies, damned lies, and statistics."

Without giving us the details of your "25,000 publicly available arrest photos", your results are meaningless. You should have spent more than $12.33 and hired someone who understands statistics.


The 25k available criminal records are part of the Amazon tool that the government plans to use. The ACLU didn't just pick a random database. They are replicating when the government who purchased this technology would use this same database in identifying citizens. If there are such a high percent of inaccurate matches, imagine what would it show when applied in the general public.


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