JOURNAL ARTICLE
PRESERVING WHAT'S REAL: THE NO FAKES ACT AS A FEDERAL RIGHT AGAINST UNAUTHORIZED DIGITAL REPLICAS.
Published In: University of Toledo Law Review, 2026, v. 57, n. 3. P. 519 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Moyer, Kelsey 3 of 3
Abstract
The article focuses on the NO FAKES Act, a proposed federal law designed to protect individuals’ voice and visual likeness rights against unauthorized digital replicas created using generative artificial intelligence (AI). It outlines the limitations of current state right of publicity (ROP) laws, which vary widely and often fail to address AI-driven deepfakes, and discusses how the NO FAKES Act seeks to establish a uniform federal “digital replication right” that applies to all individuals, living or deceased, with provisions for licensing and enforcement. The Act aims to balance protection against unauthorized use with First Amendment considerations, includes safe harbor provisions for online platforms, and proposes partial preemption of state laws to create a national standard while allowing states to maintain broader protections. The article also examines challenges related to liability, potential over-censorship, international enforcement, and the need to foster innovation alongside individual rights, concluding that while the NO FAKES Act is not perfect, it represents a significant step toward addressing the legal gaps posed by AI-generated digital replicas. [Extracted from the article]
Additional Information
- Source:University of Toledo Law Review. 2026/04, Vol. 57, Issue 3, p519
- Document Type:Article
- Subject Area:Politics and Government
- Publication Date:2026
- ISSN:0042-0190
- Accession Number:193025594
- Copyright Statement:Copyright of University of Toledo Law Review is the property of University of Toledo, College of Law and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.