Probabilistic Analysis of Copyright Disputes and Generative AI Safety

TL;DR

This paper develops a probabilistic framework for analyzing copyright disputes and evaluates the copyright safety of generative AI, including the controversial inverse ratio rule and the Near Access-Free condition.

Contribution

It formalizes evidentiary principles and the inverse ratio rule in probabilistic terms, and critically assesses the effectiveness of the NAF condition for AI copyright safety.

Findings

The inverse ratio rule is valid when properly defined.

The NAF condition has limited justifiability.

Probabilistic analysis reveals limitations in current AI copyright safety strategies.

Abstract

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.