用概率模型分析版权纠纷与生成式AI安全，验证了争议规则的合理性。
Probabilistic Analysis of Copyright Disputes and Generative AI Safety
- 将法律证据原则转化为概率模型，形式化处理版权争议。
- 证明'逆比规则'在正确定义下具有合法性，但存在适用边界。
- 揭示生成式AI的'近似无访问'条件在版权安全上效果有限。
本文提出一种概率方法，用于分析版权侵权争议。通过将判例法塑造的证据原则形式化为概率表述，研究了部分法院采纳的有争议的“逆比规则”。尽管该规则受到广泛批评，但形式化证明表明，在正确界定条件下其具有有效性。进一步地，该概率框架被应用于评估生成式AI的版权安全性。具体而言，对先前提出的“近似无访问”（NAF）条件进行了检验，结果发现其在正当性与实际效用方面均存在局限。
原文摘要 · Abstract (English)
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.
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