Probabilistic Analysis of Copyright Disputes and Generative AI Safety
<|reference_start|>Probabilistic Analysis of Copyright Disputes and Generative AI Safety: This paper presents a probabilistic approach to analyzing copyright infringement disputes by formalizing relevant judicial principles within a coherent framework based on the random-worlds method. The approach provides a structured analysis of key evidentiary principles, with particular emphasis on the "inverse ratio rule"--a controversial doctrine adopted by some courts. Although this rule has faced significant criticism, a formal proof demonstrates its validity, provided it is properly defined. Additionally, the paper examines the heightened copyright risks posed by generative AI, highlighting how extensive access to copyrighted material by generative models increases the risk of infringement. Utilizing the probabilistic approach, the Near Access-Free (NAF) condition, previously proposed as a potential mitigation strategy, 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 demonstrate how a rigorous probabilistic approach can advance our understanding of copyright jurisprudence and its interaction with emerging technologies.<|reference_end|>
@article{chiba-okabe2024probabilistic,
    title={Probabilistic Analysis of Copyright Disputes and Generative AI Safety},
    author={Hiroaki Chiba-Okabe},
    journal={arXiv preprint arXiv:2410.00475},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00475},
    primaryClass={cs.CY cs.AI}
}