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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The big question is: Did the ACLU use the Amazon recommended match confidence level of 99%? Why is this key factor not published? Please publish this information and if you did not use 99%, then retract this flawed study. Are they being honest with us or purposely publishing a terribly misleading fake news study?


Amazon’s Face Recognition used by the Precrime Division correctly identified 28 members of Congress who are going to commit a crime. What's wrong with that?


Please read the article more carefully. The database is not Rekognition - that is the tool used. They chose 25,000 photos - from where we don't know and with what distribution of demographics we don't know...


I note a strong gender bias in these results as well. Although 20% of congress are women only 3% of those identified as "criminal" in this study were women. Perhaps there is bias in the training set towards males and people of color. What was the distribution of the training set photos among the demographics within the US population. What was the distribution of the training set photos among the demographics of those convicted of crimes? Point being that any bias in the training set of these systems will result in bias of the results. Your comment "we built a face database and search tool using 25,000 publicly available arrest photos." does not provide evidence that a fair and unbiased training set was used. Don't complain about biased law enforcement when there is no evidence your work is not biased.


A truly excellent example of how to misuse technology - if you don't know what you're doing (and clearly whoever set this "test" up did not) you shouldn't be publishing. Perhaps Congress SHOULD pass a law limiting access to this technology only to those with demonstrated experience and capability to use it correctly and allowing publication only after review by someone with the expertise to correct fundamental errors prior to allowing publication.


If you cant do the face Rekognition, don't do the crime!


So, they set the settings to 80% accuracy, and ended up with an accuracy of 95%...and you're unhappy at the 5% error rate?


Fake News -> What they dont tell you in this article is API tells you a percentage of confidence of the match. It could be a 1% match which of course you can throw out... Once again deception to drive agenda. Shame on people for not digging deeper.


I would love to see the result of the scan for the rest of the government, and all of the ACLU chapters as well. For that matter, why not any public organization especially ones that take our money for "supposed" good causes!

AI Not Yet

AI has promise across many sectors. However, AI in protected civil liberties is simply not there. An example includes scanning individual faces in protected areas without their consent. Too make matters worse, tax dollars will fund the technology.

AI is good for Alexa but not for my Constitutional Protected Liberties. No way. Ever.


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