In 2017, the Washington County Sheriff’s Office, just outside Portland, Oregon, wanted to find a man covered in dollar bills. The unidentified man, whose profile photo showed him lying on a bed covered in paper money, had been making concerning posts on Facebook, the sheriff’s office said. So the department ran the image through a powerful new facial recognition and analysis system built by Amazon, called Rekognition, and used Rekognition to compare it with booking photos used by the department. According to Chris Adzima, a senior systems information analyst, the officers “found a close to 100% match.”
find-and-seek adventure mobile app designed for kids, is likely inadvertently training the Washington County Sheriff Office’s surveillance operations. . Before we felt comfortable using Rekognition to investigate their AGR, we wanted to ensure that our research would not inadvertently optimize a surveillance system that we’re simply not sure should have existed in the first place. To opt out, Jezebel contacted Amazon’s Technical Support, who in turn contacted the Rekognition engineers.
We sourced our binary dataset by using the captions from the Broadly photos to identify visually similar photos of gender-conforming individuals on Shutterstock. For instance, if a photo from Broadly’s dataset was captioned “a transmasculine person drinking a beer at a bar” we would scrape Shutterstock for photos of “a man drinking a beer at a bar.”
Though our data sets were admittedly limited, the difference in how Rekognition performs on a data with trans and nonbinary individuals is alarming. More concerning however is that Rekognition misgendered 100% of explicitly nonbinary individuals in the Broadly dataset. This isn’t because of bad training data or a technical oversight, but a failure in engineering vocabulary to address the population.
Morgan Klaus Scheuerman is a PhD student in information science at the University of Colorado-Boulder. He and his collaborators have been conducting a large-scale analysis of how individuals of different gender identities are classified, correctly or otherwise, in AGR systems, particularly large-scale, cloud-based systems like Amazon and Microsoft.
AGR systems have been designed under the presumption that gender is physiologically-rooted wherein the body is the source of a binary gender, male or female
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