Pamela Samuelson | Will Copyright Derail Generative AI Technologies? | Stanford HAI
Stanford
University
  • Stanford Home
  • Maps & Directions
  • Search Stanford
  • Emergency Info
  • Terms of Use
  • Privacy
  • Copyright
  • Trademarks
  • Non-Discrimination
  • Accessibility
© Stanford University.  Stanford, California 94305.
Skip to content
  • About

    • About
    • People
    • Get Involved with HAI
    • Support HAI
    • Subscribe to Email
  • Research

    • Research
    • Fellowship Programs
    • Grants
    • Student Affinity Groups
    • Centers & Labs
    • Research Publications
    • Research Partners
  • Education

    • Education
    • Executive and Professional Education
    • Government and Policymakers
    • K-12
    • Stanford Students
  • Policy

    • Policy
    • Policy Publications
    • Policymaker Education
    • Student Opportunities
  • AI Index

    • AI Index
    • AI Index Report
    • Global Vibrancy Tool
    • People
  • News
  • Events
  • Industry
  • Centers & Labs
Navigate
  • About
  • Events
  • AI Glossary
  • Careers
  • Search
Participate
  • Get Involved
  • Support HAI
  • Contact Us

Stay Up To Date

Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.

Sign Up For Latest News

Your browser does not support the video tag.
eventSeminar

Pamela Samuelson | Will Copyright Derail Generative AI Technologies?

Status
Past
Date
Wednesday, November 13, 2024 12:00 PM - 1:00 PM PST/PDT
Location
Gates Computer Science Building Room 119
Topics
Law Enforcement and Justice

At last count, thirty lawsuits are challenging the legality of using in-copyright works to train generative AI models. While the lawsuits are still in early stages, fair use defenses are likely to be the main focus of judicial analyses of their merits. This talk will explain what judges have decided so far, that is, what issues are now out of those cases and what issues remain. Although generative AI developers point to some precedents that support their fair use defenses, this talk will explain why judges may find them not as helpful as the developers hope. At this point, some cases seem stronger than others. Not only are the plaintiffs seeking very large damage awards, but also some want courts to order models trained on the plaintiffs' data to be destroyed, and courts have power to issue such orders. The broader impacts of these lawsuits for academic research and development are at present invisible to the courts.

Speakers
Pamela Samuelson
Richard M. Sherman, Distinguished Professor of Law, UC Berkeley Law; Professor, UC Berkeley School of Information; Co-Director, Berkeley Center for Law & Technology
Share
Link copied to clipboard!
Event Contact
Annie Benisch
abenisch@stanford.edu
More from HAI and SDS seminars
  • Inside the 2026 AI Index Report | Stanford HAI
    SeminarMay 20, 202612:00 PM - 1:15 PM
    May
    20
    2026

    The AI Index, currently in its ninth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.

Related Events

Eyck Freymann | AI and Strategic Stability: A Framework for U.S.–China Technology Competition
SeminarMay 27, 202612:00 PM - 1:15 PM
May
27
2026

Strategic stability exists when neither side thinks it can improve its strategic outcome by striking first.

Seminar

Eyck Freymann | AI and Strategic Stability: A Framework for U.S.–China Technology Competition

May 27, 202612:00 PM - 1:15 PM

Strategic stability exists when neither side thinks it can improve its strategic outcome by striking first.

Ashesh Rambachan | From Next-Token Prediction to Automatic Induction of Automata
Apr 13, 202612:00 PM - 1:00 PM
April
13
2026

Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.

Event

Ashesh Rambachan | From Next-Token Prediction to Automatic Induction of Automata

Apr 13, 202612:00 PM - 1:00 PM

Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.

Caroline Meinhardt, Thomas Mullaney, Juan N. Pava, and Diyi Yang | How Can AI Support Language Digitization and Digital Inclusion?
SeminarApr 15, 202612:00 PM - 1:15 PM
April
15
2026

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.

Seminar

Caroline Meinhardt, Thomas Mullaney, Juan N. Pava, and Diyi Yang | How Can AI Support Language Digitization and Digital Inclusion?

Apr 15, 202612:00 PM - 1:15 PM

What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.