Attribution decay means AI images may have no responsible training source
A new study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) introduces a measurable phenomenon the researchers call attribution decay. As the number of training images grows, the influence of any single example on a generated output shrinks, often to the point of being unmeasurable. The finding, published