Anthropic Seeks Dismissal of Copyright Lawsuit Over AI Training
Anthropic has asked a California federal court to dismiss a lawsuit filed by a group of authors who allege that the company violated their copyrights by using their works to train its AI model, Claude. Reuters reports that Anthropic argues its use of the authors' books constitutes fair use, as it transforms the original works into something new.
The lawsuit, filed by writers Andrea Bartz, Charles Graeber, and Kirk Wallace Johnson, claims that Anthropic used pirated versions of their works to train Claude. Anthropic contends that its training process is protected under the copyright doctrine of fair use, which allows for the unauthorized use of copyrighted material under specific circumstances. The company asserts that its AI training is a "transformative" act that U.S. copyright law encourages to promote human creativity.
The case, Bartz v. Anthropic PBC, is being heard in the U.S. District Court for the Northern District of California. Anthropic's legal team argues that the AI model's purpose is fundamentally different from allowing users to read the plaintiffs' books, thus supporting their fair use claim.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like AI Policy Brief or Daily AI Brief.
Also, consider following us on social media:
More from Regulation
Oct 24 FBI Considers Espionage Charge Against Former OpenAI Engineer Oct 24 California bill would require disclosure of AI safety computing Oct 24 UK commits £600 million to AI biosecurity and cyber defense Oct 24 Ron Wyden proposes data center tax and AI worker aid Oct 23 GSA Signs First Direct OneGov Small Business Deal With (US)AIAI Policy Brief
Weekly report on AI regulations, safety standards, government policies, and compliance requirements worldwide.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Canadian News Publishers Sue Anthropic Over Claude Training
Anthropic Seeks Rehearing in Pentagon Blacklisting Case
Anthropic to Revise Leaked AI Safety Bill After Backlash
Reservoir Sues Suno and Anthropic Over AI Training
Anthropic publishes its first Public Benefit Report, including where it fell short
Daily AI Brief: the AI news that matters, in your inbox.