Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity

By Sarah Scheffler, Eran Tromer, Mayank Varia. 2022

25 citations recorded by the source corpus.

A central notion in U.S. copyright law is judging the substantial similarity between an original and an (allegedly) derived work. Capturing this notion has proven elusive, and the many approaches offered by case law and legal scholarship are often ill-defined, contradictory, or internally-inconsistent. This work suggests that key parts of the substantial-similarity puzzle are amenable to modeling inspired by theoretical computer science. Our proposed framework quantitatively evaluates how much ''novelty'' is needed to produce the derived work with access to the original work, versus reproducing it without access to the copyrighted elements of the original work. ''Novelty'' is captured by a computational notion of description length, in the spirit of Kolmogorov-Levin complexity, which is robust to mechanical transformations and availability of contextual information. This results in an actionable framework that could be used by courts as an aid for deciding substantial similarity. We evaluate it on several pivotal cases in copyright law and observe that the results are consistent with the rulings, and are philosophically aligned with the abstraction-filtration-comparison test of Altai.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Social Sciences & Humanities

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Citations in the corpus

Foundation Models and Fair Use (2023, background): “Others have examined how machine learning or algorithms can be used for mitigating infringement risk at a high level, including Elkin-Koren (2017); Scheffler et al. (2022).”