When AI Art Has No Author: Study Finds Generated Images Often Can’t Be Traced to Training Data

Summary

MIT CSAIL researchers found that at large scales, you can often remove any single image from AI training data, every image by a given artist, or every photograph of a given person, and the generated output won’t change appreciably. At top left is an image generated by a model trained on public domain artwork created by 744 artists. The others are a sampling of images that would have been generated had any one of the 744 artists been omitted from the training set. Image credit: Collage courtesy of the researchers, showing images generated by AI. By Rachel Gordon When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility. New work from a team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It’s not that the tools for finding it are inadequate. The connection itself has disappeared. The scientists identified a phenomenon they call attribution decay, where the more data a generative model is trained on, the less any individual training example matters to any particular output. It feels counterintuitive, but at sufficiently large scales, they find, you can often remove any single image from the training data, or every image by a given artist, or every photograph of a given person, and the generated sample doesn’t change. And if removing something changes nothing, the researchers argue, it can’t be said to be responsible for anything. “If you take away a piece of data and the output of the model doesn’t change, then that piece of data didn’t affect the output,” says Zheng Dai, former MIT CSAIL researcher and lead author on the work. “So it doesn’t make much sense to attribute the output to that piece of data. And if you then do this one at a time for every other piece of data and find that the output doesn’t change for any of them either, then it doesn’t make much sense to attribute the output to any one of them.” “All previous methods were approximate,” says MIT Professor David Gifford, who is an MIT CSAIL principal investigator. “They really could not absolutely show that deleting individual things did not cha…