Algorithmenzur face detection does not work well with all tones and Geschlechterngleich. This is eineStudie by computer scientist Joy Buolamwini of Massachusetts Institute OfTechnology (MIT). As a result, white people are werdenmännliche by some widely used machine-learning Systemenam, higher error rate, darker Hautfarbeund is highest in black women.
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Boulamwinihat algorithms investigated to assign faces to a gender, and Zwardie machine learning systems from Microsoft, IBM and face . The latter is used in products from customers such as Lenovo and Dell. The Geschlechtvon was about 99 percent of white men from a sample of 385 Fotoserkannt. For white women, rate dropped to 93 percent, with 296ausgesuchten images. Out of 318 images, only 88 percent of männlichenPersonen with darker skin color were recognized as men. With 271 images from SchwarzenFrauen, facial recognition algorithms are only 75 percent accurate.
Dabeierzielten algorithms of Microsoft nor better results GegenüberIBM and face : They did not recognized 21 percent of dark-skinned women, while error rate among competitors was 35 percent. It has been nurvermutet that algorithms worked in different population groups, said Sorelle Friedler of Haverford College interview with New York Times. DieStudie context empirically.
Less reference material for black women
There are several reasons for inaccuracies in facial recognition. For one, algorithms are only as clever as data y allocated – and mirror, mostly to developers mselves unconsciously, ir racist or sexist Tendenzenwider. According to New York Timesauf, about 75 percent of a collection of reference images provided alsTrainingsmaterial for various applications are depicted in men. In turn, 80 percent are white.
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In past years, re have always been reports of algorithms that have not been sufficiently trained for diversity. In 2015, Google described PhotosBilder a black woman with Tag “gorilla” – and thus triggered a wider debate. Not much better was developers of app Beauty.ai, who wanted to meet objective statements about beauty, among 40 winners but only one black woman was found. Zuletztgab reports that face detection of iphone x can be asiatischenGesichterneinfacher.
In current case, technical aspects could also have an influence: Soerleichtern contrast algorithms distinguishing shapes. These are white faces higher. Moreover, variety of hairstyles in Frauendie assignment to one sex could be difficult. Both IBM and Microsoft reported in response to study to resolve any prejudices in ir Softwarezu.
Buolamwini wants to raise public awareness of problems of Ihrnachgewiesene. As a weiblicheAfroamerikanerin during her studies, she herself had experienced that algorithms had recognized her Gesichtnicht. She told NewYork Times, “Okay, that’s serious. Time to do something. ” The result: The Algorithmic Justice League, a project dedicated to problem.
