Data Bytes - Authorship, Copyright, and AI: Legal and Scholarly Perspectives (Zoom)

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, Getty Images v. Stability AI, 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. We will discuss the implications of these cases for academia; for example, what happens when scholars use AI outputs that reproduce copyrighted passages or images without citation or permission? What tools and best practices are emerging for managing text corpora, including in academic data mining? What ethical questions arise around style appropriation and attribution? 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.