Does generative AI actually copy artists? Researchers say it’s up for debate

FastCompany: Generative AI has long been accused of copying artists’ work outright (see the numerous copyright lawsuits winding their way through court). But a new study out of MIT makes a very different argument—one that could complicate how those cases hold up. In the study, published in Nature , the MIT researchers Zheng Dai and David Gifford set out to to test whether a generated image can be traced back to a single piece of training data. They were looking specifically at diffusion models, the systems most often used for generating images and video. Their finding? It comes down to how big the training data set is.

They found that the more data a model is trained on, the harder it becomes to attribute its output to any particular piece of training data. In fact, when they removed a specific piece of training data from the set, they found that it had little to no bearing on the updated output of the model.

As the researchers put it: “We can often omit any sample or creator from the training data without affecting a generated sample.” This trend, what the researchers call “attribution decay,” means that artificial intelligence can produce an image that resembles a particular artist’s work, while having no provable causal link to that artist’s actual contribution to the training data. The researchers suspect that this happens because larger datasets tend to contain a lot of visual redundancy; many different images share overlapping features, so no single image is responsible for the DNA of an AI-generated image.

They got the same result again and again, across dozens of experiments. But they’re careful to treat this as their best explanation rather than something they’ve directly proven (more on that below). How this works To get to this finding, the researchers needed a way to test cause and effect precisely, because diffusion models don’t work like a database you can just delete files from. During training, a model doesn’t store copies of the images it sees. Rather, it adjusts millions of internal numerical settings based on patterns across the entire training set, and later uses those settings to turn random noise into a new image.

That means you can’t simply delete one training image and expect a straightforward before-and-after comparison. The influences of all the training images are normally tangled together across the whole model. Instead, the researchers built models out of separate components, each trained independently on a different slice of the data, then combined. That structure let them cleanly remove the influence of one artist, person, or image by switching off just the components that had been exposed to it, without retraining the entire model from scratch.

They call this technique “ablation.” AI image generators learn by studying huge numbers of pictures and learning to reconstruct them. Dai and Gifford generated an image using the full training set, then regenerated it with one artist, person, or image removed, while everything else remained the same. If the newly generated results didn’t change, they could surmise that the missing piece wasn’t the cause of that particular output; the model would have generated a nearly identical image regardless.

Article truncated for readability. Read the full piece →

The article addresses a significant and timely issue regarding copyright and generative AI in the design industry, making it highly relevant and impactful for brand strategy professionals.