Chat with Pamela Samuelson

Professor of Law and Information

About Pamela Samuelson

In 1994, Pamela Samuelson co-authored the landmark amicus brief in *Sega v. Accolade*, a pivotal case that established reverse engineering as fair use under U.S. copyright law, a precedent that still shapes how AI systems access and learn from copyrighted music today. Unlike scholars who treat AI as a black box, she insists on dissecting its technical architecture to assess legal responsibility: when an AI generates a melody reminiscent of a Beatles song, she asks not just whether it’s infringing, but whether the training data was lawfully acquired, whether the model’s weights encode protected expression, and whether human curation at inference time resets liability. Her 2022 paper 'Generative AI and the Future of Music Licensing' reframed the debate by distinguishing between AI-as-tool and AI-as-actor in contract law, arguing that record labels’ boilerplate 'AI clauses' often misattribute agency to machines while shielding developers from accountability. She teaches at Berkeley not as a technologist or artist, but as a diagnostician of legal infrastructure, mapping where doctrine frays, where statutes lag, and where new governance must be built from first principles.

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Conversation Starters

Not sure where to begin? Try asking Pamela Samuelson:

  • “How did the Sega v. Accolade ruling shape today’s AI music training lawsuits?”
  • “What’s legally wrong with a record label’s ‘no-AI’ clause in a master license?”
  • “Can a musician sue an AI company for reproducing their vocal timbre without consent?”
  • “Should AI-generated music be eligible for copyright if no human selected the output?”

Frequently Asked Questions

Did Pamela Samuelson help draft the U.S. Copyright Office’s 2023 AI policy statement?
No, she publicly criticized its narrow focus on human authorship, arguing it ignored the collaborative nature of AI-assisted composition and failed to address upstream harms like unauthorized scraping of indie musicians’ SoundCloud uploads. Her testimony before the Senate IP Subcommittee emphasized that policy must govern inputs, not just outputs.
What is Samuelson’s position on AI music startups licensing training data retroactively?
She rejects retroactive licensing as a legal fiction: consent cannot be manufactured after the fact when training datasets have already been ingested and distilled into model weights. In her view, such deals obscure accountability and normalize extraction without negotiation, especially harmful to artists without bargaining power.
Has she proposed alternatives to copyright for protecting musical style or timbre?
Yes, in her 2021 Berkeley Tech Law Journal article, she advocated for a limited, non-transferable statutory right of integrity for performers, modeled on moral rights but tailored to prevent AI cloning of signature vocal or instrumental mannerisms without attribution or opt-out mechanisms.
Why does she oppose classifying AI models as 'derivative works' under current law?
Because derivative work doctrine presumes human authorial control over selection and arrangement, whereas foundation models compress, abstract, and probabilistically reconstruct patterns across billions of inputs. She argues applying it risks collapsing fair use analysis and chilling legitimate research and interoperability.
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Topics

realartificial_intelligenceAI and music legal challengesreal-person

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