By Clearview AI BLOG
In 2018, the American Civil Liberties Union (ACLU) conducted a test of Amazon's Rekognition facial recognition technology on the California State Assembly. The ACLU claimed that the technology had a high rate of false positives when comparing photos of every member of the California state legislature against a database of 25,000 public mugshots, and that it disproportionately misidentified people of color.
The test and its results were widely reported in the media and sparked a broader debate about the potential risks and benefits of facial recognition technology.
However, Amazon disputed the ACLU's methodology and findings, stating that the organization had used the technology improperly and had not followed best practices in setting up and using the system. Amazon also pointed out that the ACLU had used a low confidence threshold in its testing, which would have resulted in a higher rate of false positives.
To understand this determination, it's important to understand the two main applications of facial recognition technology: verification and identification.
In verification, the technology is asked to determine whether a specific person's face matches a reference image, and will return a yes or no answer. The technology is looking for a 100 percent match, and the technology makes the decision if it is a match. Because the technology is asked to make that 100 percent match determination, instances of false-positives or false-negatives are directly associated with the technology’s performance.