Modeling Legal Uncertainty in AI-Generated Content Ownership: A Bayesian Network Approach to Intellectual Property Rights Allocation.

Li Y

DOI: 10.3791/71101
Published: 2026 Jun 12
Container: Journal of visualized experiments : JoVE
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3791/71101,
  title = {Modeling Legal Uncertainty in AI-Generated Content Ownership: A Bayesian Network Approach to Intellectual Property Rights Allocation.},
  author = {Li Y},
  year = {2026},
  journal = {Journal of visualized experiments : JoVE},
  doi = {10.3791/71101},
  url = {https://doi.org/10.3791/71101}
}

RIS

TY  - JOUR
TI  - Modeling Legal Uncertainty in AI-Generated Content Ownership: A Bayesian Network Approach to Intellectual Property Rights Allocation.
AU  - Li Y
PY  - 2026
JO  - Journal of visualized experiments : JoVE
DO  - 10.3791/71101
UR  - https://doi.org/10.3791/71101
ER  -

APA

Y, L. (2026). Modeling Legal Uncertainty in AI-Generated Content Ownership: A Bayesian Network Approach to Intellectual Property Rights Allocation.. Journal of visualized experiments : JoVE. https://doi.org/10.3791/71101

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pubmed · retrieved 2026-09-25T03:37:47.220Z