But last week every human face included in ImageNet suddenly disappeared--after the researchers who manage the data set decided to blur them. 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. In a research paper, posted along with the update to ImageNet, the team behind the data set explains that it blurred the faces using Amazon's AI service Rekognition; then, they paid Mechanical Turk 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. In July 2020 Vinay Prabhu, a machine learning scientist at UnifyID and Abeba Birhane, a PhD candidate at University College Dublin in Ireland, published research showing they could identify individuals, including computer science researchers, in the data set. But last week every human face included in ImageNet suddenly disappeared--after the researchers who manage the data set decided to blur them.

In 2012, artificial 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 ImageNet, is still used in thousands of AI 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 Olga Russakovsky, an assistant professor at Princeton University and one of those responsible for managing ImageNet.