Probabilistic Analysis of Copyright Disputes and Generative AI Safety

• URL: http://arxiv.org/abs/2410.00475v4
• Date: Sat, 25 Jan 2025 02:14:36 GMT
• Title: Probabilistic Analysis of Copyright Disputes and Generative AI Safety
• Authors: Hiroaki Chiba-Okabe,
• Abstract summary: This paper presents a probabilistic approach to analyzing copyright infringement disputes.<n>The usefulness of this approach is showcased through its application to the inverse ratio rule''
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a probabilistic approach to analyzing copyright infringement disputes. Under this approach, evidentiary principles shaped by case law are formalized in probabilistic terms, allowing for a mathematical examination of issues arising in such disputes. The usefulness of this approach is showcased through its application to the ``inverse ratio rule'' -- a controversial legal doctrine adopted by some courts. Although this rule has faced significant criticism, a formal proof demonstrates its validity, provided it is properly defined. Furthermore, the paper employs the probabilistic approach to study the copyright safety of generative AI. Specifically, the Near Access-Free (NAF) condition, previously proposed as a strategy for mitigating the heightened copyright infringement risks of generative AI, is evaluated. The analysis reveals that, while the NAF condition mitigates some infringement risks, its justifiability and efficacy are questionable in certain contexts. These findings illustrate how taking a probabilistic perspective can enhance our understanding of copyright jurisprudence and its interaction with generative AI technology.

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