How ImageNet sees me. On the left: "beard" / On the right: "Bedouin, Beduin"

Maybe you've seen images like these floating around social media this week: photos of people with lime-green boxes around their heads and funny, odd or in some cases super-offensive labels applied.

What's happening: They're from an interactive art project about AI image recognition that doubles as a commentary about the social and political baggage built into AI systems.

Why it matters: This experiment — which will only be accessible for another week — shows one way that AI systems can end up delivering biased or racist results, which is a recurring problem in the field.

  • It scans uploaded photos for faces and sends them to an AI object-recognition program that uses ImageNet, the gold-standard dataset for training such programs.
  • The program matches the face with the closest label from WordNet, a project that started in the 1980s to map out word relationships throughout the English language, and applies it to the image.

Some people got generic results, like "woman" or "person." Others received hyper-specific labels, like "microeconomist." And many got some pretty racist stuff.

"The point of the project is to show how a lot of things in machine learning that are conceived of as technical operations or mathematical models are actually deeply social and deeply political," says Trevor Paglen, the MacArthur-winning artist who co-developed the project with Kate Crawford of the AI Now Institute.

  • The experiment and accompanying essay reveal the assumptions that go into building AI systems.
  • Here, the system depends on judgment calls from the people who originally labeled the images — some straightforward, like "chair"; others completely unknowable from the outside, like "bisexual."
  • From those image–label pairs, AI systems can learn to label new photos that they've never seen before.

But, but, but: This is an art project, not an academic takedown of ImageNet, which is mostly intended to detect objects rather than people. Some AI experts have criticized the demonstration for giving a false impression of the dataset.

This week ImageNet responded to the project, which Paglen says is currently being accessed more than 1 million times per day.

  • The ImageNet team says it's making changes to person-related image labels, in part by removing 600,000 potentially sensitive or offensive images — more than half of the images of people in the dataset.

Bonus: When Axios' Erica Pandey uploaded a photo of herself, the ImageNet experiment classified her as a "flibbertigibbet," which is disrespectful but a great word.

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