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 16 HKMA Introduces ART Framework for AI Use in Banking Oct 15 UK Medicines Regulator Plans AI Safety Tools by 2030 Oct 15 OpenAI Agrees Not to Use Apple Trade Secrets During Lawsuit Oct 15 Financial Stability Board Publishes 12 AI Practices for Financial Firms Oct 15 Singapore Launches Three AI Safety InitiativesAI 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
AI Companies Face Antitrust Lawsuit Over Slowdown Calls
Anthropic Warns AI Agents Could Create Uncertain Legal Liability
Anthropic Picks Accenture for AI Safety Testing
Anthropic Cuts Internet Access for Internal AI Evaluations
Anthropic IPO Prospectus Warns of Existential AI Risks
Daily AI Brief: the AI news that matters, in your inbox.