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als RSS Feed abonnieren📚 Proctorio Is Using Racist Algorithms To Detect Faces
💡 Newskategorie: IT Security Nachrichten
🔗 Quelle: news.slashdot.org
An anonymous reader quotes a report from Motherboard: Students of color have long complained that the facial detection algorithms Proctorio and other exam surveillance companies use fail to recognize their faces, making it difficult if not impossible to take high-stakes tests. Now, a software researcher, who also happens to be a college student at a school that uses Proctorio, says he can prove the Proctorio software is using a facial detection model that fails to recognize Black faces more than 50 percent of the time. Akash Satheesan, the researcher, recently published his findings in a series of blog posts. In them, he describes how he analyzed the code behind Proctorio's extension for the Chrome web browser and found that the file names associated with the tool's facial detection function were identical to those published by OpenCV, an open-source computer vision software library. Satheesan demonstrated for Motherboard that the facial detection algorithms embedded in Proctorio's tool performed identically to the OpenCV models when tested on the same set of faces. Motherboard also consulted a security researcher who validated Satheesan's findings and was able to recreate his analysis. [...] Satheesan tested the models against images containing nearly 11,000 faces from the FairFaces dataset, a library of images curated to contain labeled images representative of multiple ethnicities and races. The models failed to detect faces in images labeled as including Black faces 57 percent of the time. Some of the failures were glaring: the algorithms detected a white face, but not a Black face posed in a near-identical position, in the same image. The pass rates for other groups were better, but still far from state-of-the-art. The models Satheesan tested failed to detect faces in 41 percent of images containing Middle Eastern faces, 40 percent of those containing white faces, 37 percent containing East Asian faces, 35 percent containing Southeast Asian or Indian faces, and 33 percent containing Latinx faces.
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