Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2025

Abstract

Copyright enforcement rests on an evidentiary bargain: a plaintiff must show both the defendant's access to the work and substantial similarity in the challenged output. That bargain comes under strain when AI systems are trained through multi-generational pipelines with recursive synthetic data. As each successive model is tuned on the outputs of its predecessors, any copyrighted material absorbed by an early model is further diffused into deeper statistical abstractions. The result is an evidentiary blind spot where overlaps that emerge look coincidental, while the chain of provenance is too attenuated to trace.These conditions are ripe for "copyright laundering"—the use of multi-generational synthetic pipelines, an "AI Ouroboros," to render traditional proof of infringement impracticable. This Article adapts the "fruit of the poisonous tree" (FOPT) principle to propose a novel AI-FOPT standard: if a foundational AI model's training is adjudged infringing (either for unlawful sourcing or for non-transformative ingestion that fails fair-use), then subsequent AI models principally derived from the foundational model's outputs or distilled weights carry a rebuttable presumption of taint. The burden then shifts to the downstream developers—those who control the evidence of provenance—to restore the evidentiary bargain by affirmatively demonstrating a verifiably independent and lawfully sourced lineage or a curative rebuild, without displacing fair-use analysis at the initial ingestion stage. Absent such proof, commercial deployment of tainted models and their outputs is actionable.Drawing on legal precedents, this Article develops the standard by specifying its trigger, presumption, and concrete rebuttal paths (such as independent lineage or verifiable unlearning); addresses counterarguments concerning chilling innovation and fair use; and demonstrates why this lineage-focused approach is both administrable and essential to preserve copyright's efficacy and incentive structure when machines train machines.

Keywords

Copyright Laundering, Fruit of the Poisonous Tree, Copyright Law, Synthetic Data, Artificial Intelligence. JEL codes: K11

Discipline

Artificial Intelligence and Robotics | Intellectual Property Law | Marketing

Research Areas

Marketing

Publication

Rutgers Computer and Technology Law Journal

Volume

52

Issue

1

First Page

61

Last Page

112

ISSN

0735-8938

Publisher

Rutgers Law School

Copyright Owner and License

Authors

Download

Share

COinS