This workshop explores the legal and scholarly implications of authorship and generative AI for faculty and students, using major lawsuits—such as Authors Guild v. OpenAI, Bartz v. Anthropic, Kadrey v. Meta, and New York Times v. OpenAI—as case studies to examine differing perspectives on fair use and infringement. AI developers argue that training is transformative: outputs are new works created for learning and research, not direct copies. Copyright holders and authors counter that their works were used without permission, sometimes sourced from pirated copies, replicating their style or even reproducing content verbatim. This discussion will begin with an overview of the U.S. Copyright Office's reports on Generative AI and Copyright, before moving into an overview and update of the major lawsuits and rulings. We will then discuss some implications of these cases for academia; for example: can my work be used for AI training? Can I use AI generated content in my work? What if AI distorts my work or reputation? Participants will leave with a clear understanding of these landmark cases, their relevance to scholarly authorship, and practical approaches for responsibly integrating AI into research and teaching.

Explore other upcoming Data Bytes sessions in the Library calendar.