Probabilistic Analysis of Copyright Disputes and Generative AI Safety
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917206826156032 |
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| author | Chiba-Okabe, Hiroaki |
| author_facet | Chiba-Okabe, Hiroaki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Probabilistic Analysis of Copyright Disputes and Generative AI Safety Chiba-Okabe, Hiroaki Computers and Society Artificial Intelligence 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. |
| title | Probabilistic Analysis of Copyright Disputes and Generative AI Safety |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2410.00475 |