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.

翻译：本文提出一种分析版权侵权争议的概率方法。通过案例法形成的证据原则被形式化为概率术语，并对"反比规则"——一种被部分法院采纳但存在争议的法律原则——进行了检验。尽管该规则面临显著批评，但形式化证明表明，只要正确定义，该规则是有效的。该概率方法进一步用于研究生成式人工智能的版权安全性。具体而言，本文评估了先前提出的"近似无接触"条件——作为缓解生成式人工智能较高版权侵权风险的策略。分析揭示了该条件在合理性与有效性方面的局限性。