# Probabilistic Analysis of Copyright Disputes and Generative AI Safety

arXiv: https://arxiv.org/abs/2410.00475 · HTML: https://mlsanity.com/paper/2410.00475/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [On Provable Copyright Protection for Generative Models](https://mlsanity.com/paper/2302.10870/) (2023)
- [Can Copyright be Reduced to Privacy?](https://mlsanity.com/paper/2305.14822/) (2023)

## Built on by (most cited first, 2 of 2)

- [CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2502.15278/) (2025)
- [AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models](https://mlsanity.com/paper/2509.00641/) (2025)

## Abstract (arXiv)

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.