AI models trained on massive datasets are losing their ability to trace outputs back to specific training data — a phenomenon called “attribution decay.” Researchers found that removing individual images or even entire artist collections often has no effect on generated content, making it nearly impossible to assign credit or responsibility. Using a novel “diffusion ensemble” system, they proved that as models grow larger, the influence of any single training example shrinks dramatically. This threatens existing intellectual property frameworks and forces us to rethink ownership and accountability in the age of AI.
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