As Open Access (OA) is gaining momentum, institutions are engaging in partnerships to help further advancements in open publishing. Publishers are embracing Transformative Agreements, Read and Publish deals, and Subscribe to Open (S2O) models. With this new landscape comes the challenge of quantifying the impact and value on investments for both institutions and publishers alike. Metrics such as COUNTER statistics are a powerful tool for assessing the reach, engagement, and scholarly impact of OA initiatives within collaborative institutional-publisher frameworks. This session seeks to provide a comprehensive understanding of how statistics can be strategically employed to measure and communicate the value of OA in partnerships with both traditional publishers and emerging open models. Hear from stakeholders across the industry (two librarians, one small publisher, and one large publisher) for different perspectives on the proliferation of OA and how statistics can help tell a story.
Head of Sales Operations, Royal Society of Chemistry
I have been working in Sales in Academic Book Publishing for more than a decade, firstly with Elsevier S&T then with Emerald Group Publishing Limited and now at the Royal Society of Chemistry where I am responsible for global print book sales and aggregator eBook sales.
I manage... Read More →
Collection Development & Management Librarian, Professor, Portland State University
I am the Collection Development Librarian at Portland State University Library and have over 20 years of academic library experience. I have held leadership positions in ALA ALCTS, ER&L, and NASIG. In 2015, I served as the ALA-NISO representative to vote on NISO/ISO standards on behalf... Read More →
Director of Institutional Product Marketing, Wiley
I am Wiley’s resident librarian, with over 15 years of experience in libraries and scholarly publishing. As the Director of Institutional Product Marketing, I lead a global team responsible for go-to-market strategies, product positioning and messaging, sales enablement, and industry... Read More →
Jill Grogg is a Strategist with the Content & Scholarly Communication Initiatives team at LYRASIS. Previously, she was electronic resources coorindator at The University of Alabama Libraries for over a decade.
This presentation gives an overview of interactions between United States (U.S.) copyright law and artificial intelligence (AI). Current generative AI is different from past big data, in that current AI models train on full text long form written works, while in the past, datasets and structured information had the highest value. Also, in recent years, there has been extensive economic activity around AI, which newly surfaced business oriented issues. As a result, new issues have arisen regarding copyright law and AI.
Throughout 2023 and 2024, the U.S. Copyright Office held listening sessions around specific topics related to generative AI and copyright. The Copyright Office released guidance about digital replicas (ie. deep fakes) with guidance yet to come on additional topics including copyrightability of works incorporating AI-generated material, training AI models on copyrighted works, licensing considerations, and liability issues. This presentation gives a just-the-facts summary of U.S. Copyright Office activities, and of emergent case law from lawsuits related to AI and copyright.
A current parallel regulatory thrust is comprehensive U.S. federal regulation of AI ethics. The National Artificial Intelligence Initiative Act of 2020 provided funding for a 5 year roll out of AI regulation. With AI ethics regulation in rapid development in the U.S., and legally binding outcomes pending in the near future, large corporations building AI tools have a strong incentive to control the conversation and define AI ethics. By emphasizing copyright, corporations might seek to shift emphasis away from other ethical issues, such as the impacts algorithmic decision making has on people’s lives, increased surveillance, and other ethical issues. For example, it may be the case that ethics discussion is steered towards copyright law and “ethical AI” co-opted to refer to training AI models on licensed content. This presentation considers ethics more broadly, and invites participants to consider how increased focus on copyright and ethics might distract from other ethical issues.
Licensing dovetails with copyright, in that contractual obligations or contractual rights can shift what is allowable and can limit fair use or can expand what is allowed. Generative AI is largely controlled by a handful of very large corporations. High quality training data, such as scholarly articles and other high quality material written by people, tends to be controlled by not-quite-as-large corporations. For example, Google’s market cap is 2.3 trillion U.S. dollars, more than 600 times Clarivate’s market cap of 3.6 billion U.S. dollars. In order to maintain control of assets, in light of a potentially lucrative new use, academic database providers might tend to contractually limit established fair uses like text mining. This presentation brushes on text mining as fair use, and on trends in licensing restrictions.
This presentation overviews recent trends in U.S. copyright law and AI, with emphasis on developing federal regulations and guidance, AI ethics, and the legal right of scholars to do text as impacted by changing licensing practices.
