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When AI output has no traceable ancestor, creator rights must move upstream to training data, licenses, and clear model provenance records
A portrait resembles an artist’s work. A similarity tool names them. Yet removing that artist’s images from training may leave the result unchanged.
On August 18, 2026, Zheng Dai and David K. Gifford reported tests of 24 diffusion ensembles across seven public image datasets. As datasets grew, individual training units carried less causal responsibility for a sample. The study covers those settings, not every model or copyright law.
So separate four questions: What entered training? What caused the output? What does it resemble? Which rights apply? When ancestry dissolves, document datasets, licenses, exclusions, model versions, and edits.
