Pam Samuelson

Pam Samuelson, the Richard M. Sherman Distinguished Professor of Law at UC Berkeley, discusses the intricate relationship between generative AI and copyright law. She breaks down key court rulings, such as Bartz v. Anthropic, highlighting the debates on transformative use and market harm. The conversation also explores the implications of market dilution theories in lawsuits against AI companies. With insights on the evolving legal landscape and the role of the U.S. Copyright Office, Samuelson sheds light on a rapidly changing and complex field.

The only thing more impressive than the performance of generative AI systems like GPT-4 and Stable Diffusion is the sheer volume of training data that went into these systems. GPT was reportedly trained on, essentially, the entire Internet, while Stable Diffusion and other image-generation models rely on hundred of millions if not billions of existing pieces of artwork. Of course, much of this content is copyrighted, and the authors and artists whose work is being used to train these models and, potentially, threaten their own livelihoods are paying attention. A number of high-profile lawsuits are making their way through the courts, and the outcome of these cases could hugely shape, and potentially even stop, progress in machine learning.To explore these issues, Alan Rozenshtein, Associate Professor of Law at the University of Minnesota and Senior Editor at Lawfare, spoke with Pam Samuelson, the Richard M. Sherman Distinguished Professor of Law at the University of California at Berkeley and one of the pioneers in the study of digital copyright law. She's just published a new piece in the journal Science titled "Generative AI meets copyright,” in which she analyzes the current litigation around generative AI and where it might lead.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.

Pam Samuelson, the Richard M. Sherman Distinguished Professor of Law at UC Berkeley, specializes in copyright law and AI's legal implications. She discusses recent court rulings like Bartz v. Anthropic, probing whether training AI on copyrighted material constitutes fair use. The conversation highlights the balance between protecting creators' rights and promoting innovation, while also exploring the transformative nature of AI outputs. Key cases like Warhol vs. Goldsmith are examined for their impact on copyright law, making this a must-listen for anyone interested in the future of intellectual property.