This presentation provides a comprehensive overview of the copyright issues surrounding generative AI-training data, output ownership, and ongoing litigation. It addresses the copyrightability of AI-generated output under the human-authorship requirement and reviews U.S. Copyright Office guidance on registering works containing AI-generated material across the spectrum of human involvement.
It then examines training data and fair use-the mechanics of training large language models (LLMs) and image generators on copyrighted works and the four fair-use factors, with emphasis on transformative use and market harm. The program surveys the current litigation landscape, including Getty Images v. Stability AI and Andersen v. Stability AI and Digital Millennium Copyright Act (DMCA) claims over copyright-management information (CMI), and closes with practical advice for both creators and companies.
Businesses are deploying generative tools into content pipelines while the rules governing ownership and infringement remain in active litigation. A registration denied for lack of human authorship, or a fair-use theory that collapses at trial, can undo a product strategy. This session gives practitioners a current, usable map of the copyrightability, fair-use, and litigation terrain, plus concrete risk-mitigation steps.
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