Probabilistic Analysis of Copyright Disputes and Generative AI Safety

作者：Hiroaki Chiba-Okabe · 发表于：International Conference on Artificial Intelligence and Law · 年份：2024 · DOI：10.1145/3769126.3769139 · 被引用次数：4 · 研究领域：Computer Science

This paper presents a probabilistic approach to analyzing copyright infringement disputes. Evidentiary principles shaped by case law are formalized in probabilistic terms, and the “inverse ratio rule”—a controversial legal doctrine adopted by some courts—is examined. Although this rule has faced significant criticism, a formal proof demonstrates its validity, provided it is properly defined. The probabilistic approach is further employed 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 limitations in its justifiability and efficacy.