Substantial Similarity in Generative AI Outputs: Difficulties and Rule Reconstruction

Mingcheng Deng

doi:10.54691/8rr6cm87

Authors

Mingcheng Deng Department of Logistics Management, Nanchang Jiaotong Institute, Nanchang, Jiangxi Province, 330100, China

DOI:

https://doi.org/10.54691/8rr6cm87

Keywords:

Generative AI; substantial similarity; copyright infringement; expression filtering; source inference.

Abstract

Generative AI does not make substantial similarity irrelevant. It changes the factual basis and evidentiary conditions under which the doctrine operates. A model output may resemble a prior work, but resemblance alone does not prove copying of protected expression. Training data, parametric learning, prompts, and post-processing jointly shape outputs. They therefore separate evidence of similarity from the cause of similarity. Existing scholarship has focused on training legality, copyrightability, and the allocation of responsibility. It has not fully explained how attribution should be reasoned at the output stage. This article argues that courts should reorder the inquiry within the existing infringement structure. They should filter expression first, infer source second, and apply a normative backstop last. This sequence can protect authors without turning style or genre conventions into private property.

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