Name: Researchers Blur Faces That Launched a Thousand Algorithms
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Researchers Blur Faces That Launched a Thousand Algorithms

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End UserSectorSource DataTechnology intelligence researchers engineered a big leap in computer vision thanks, in part, to an unusually large set of images—thousands of everyday objects, people, and scenes in photos that were scraped from the web and labeled by hand. That data set, known as , is still used in thousands of research projects and experiments today.But last week every human face included in ImageNet suddenly disappeared—after the researchers who manage the data set decided to blur them.Just as ImageNet helped usher in a new age of AI, efforts to fix it reflect challenges that affect countless AI programs, data sets, and products.“We were concerned about the issue of privacy,” says , an assistant professor at Princeton University and one of those responsible for managing ImageNet.ImageNet was created as part of that invited computer scientists to develop algorithms capable of identifying objects in images. In 2012, this was a very difficult task. Then a technique called , which involves “teaching” a by feeding it labeled examples, proved more adept at the task than previous approaches.Since then, deep learning has driven a renaissance in AI that also exposed the field’s shortcomings. For instance, has proven a particularly popular and lucrative use of deep learning, but it's also controversial. A number of US cities have over concerns about invading citizens’ privacy or bias, because the programs are .Today ImageNet contains 1.5 million images with around 1,000 labels. It is largely used to gauge the performance of algorithms, or to train algorithms that perform specialized computer vision tasks. Blurring the faces affected 243,198 of the images.Search our and discover stories by sector, tech, company, and more.Russakovsky says the ImageNet team wanted to determine if it would be possible to blur faces in the data set without changing how well it recognizes objects. “People were incidental in the data since they appeared in the web photos depicting these objects,” she says. In other words, in an image that shows a beer bottle, even if the face of the person drinking it is a pink smudge, the bottle itself remains intact.In , posted along with the update to ImageNet, the team behind the data set explains that it blurred the faces using Amazon’s AI service ; then, they paid workers to confirm selections and adjust them.Blurring the faces did not affect the performance of several object-recognition algorithms trained on ImageNet, the researchers say. They also show that other algorithms built with those object-recognition algorithms are similarly unaffected. “We hope this proof-of-concept paves the way for more privacy-aware visual data collection practices in the field,” Russakovsky says.It isn’t the first effort to adjust the famous library of images. In December 2019, the ImageNet team introduced by human labelers after a project called drew attention to the issue.Supersmart algorithms won't take all the jobs, But they are learning faster than ever, doing everything from medical diagnostics to serving up ads.In July 2020 , a machine learning scientist at UnifyID and , a PhD candidate at University College Dublin in Ireland, showing they could identify individuals, including computer science researchers, in the data set. They also found pornographic images included in it.Prabhu says blurring faces is good but is disappointed that the ImageNet team did not acknowledge the work that he and Birhane did. Russakovsky says a citation will appear in an updated version of the paper.Most PopularBlurring faces still might have unintended consequences for algorithms trained on the ImageNet data. Algorithms might, for example, learn to look for blurred faces when searching for particular objects.“One important problem to consider is what happens when you deploy a model that was trained on a face-blurred data set,” Russakovsky says. For example, a robot trained on the data set might be thrown off by faces in the real world.Aleksander Madry, a research scientist at MIT who has identified limitations of ImageNet, says an AI model trained on a dataset containing blurred faces might perform strangely when shown images containing faces. "Biases in data can be very subtle while having significant consequences," he says. "That's what makes thinking about robustness and fairness in the context of machine learning so tricky."Updated, 3-15-21, 11:25am ET: This article has been updated to include additional comment from Olga Russakovsky and Aleksander Madry.More Great WIRED Stories is a senior writer for WIRED, covering artificial intelligence. He was previously a senior editor at MIT Technology Review, where he wrote about fundamental advances in AI and China’s AI boom. Before that, he was an editor and writer at New Scientist. He studied anthropology and journalism in...

End UserSectorSource DataTechnology intelligence researchers engineered a big leap in computer vision thanks, in part, to an unusually large set of images—thousands of everyday objects, people, and scenes in photos that were scraped from the web and labeled by hand. That data set, known as , is still used in thousands of research projects and experiments today.But last week every human face included in ImageNet suddenly disappeared—after the researchers who manage the data set decided to blur them.Just as ImageNet helped usher in a new age of AI, efforts to fix it reflect challenges that...

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Researchers Blur Faces That Launched a Thousand Algorithms

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