MIT study finds AI image generators struggle tracing artist contributions
A new MIT study found that as AI image generators are trained on bigger and bigger datasets, it is almost impossible to trace which original images or artists actually shaped the final results.
Even if you remove a specific artist's work from the training set, the AI's output barely changes, making it tricky to figure out who deserves credit or compensation.
Larger datasets reduce artist attribution
This "attribution decay" means individual contributions get lost in the crowd as datasets grow, raising big questions about how to fairly recognize and pay artists whose work trains these systems.
The researchers tested their ideas on datasets ranging from just 256 images to more than 160,000 images, showing that the bigger the dataset, the less any single image matters.
For now, these findings only apply to image-generating AIs, not text tools like ChatGPT, but it is a big deal for anyone following art and AI debates.