Charting a Third Path: Fair Use, Open Access, and University-Built Datasets

Authors

• Dave Hansen, Authors Alliance

Document Type

Other

Date of Seminar

10-20-2026

Notes

Text and data mining is not new. Researchers have applied computational methods to large collections of copyrighted works for decades, and AI training extends that practice. In this talk, Dave Hansen, Executive Director of Authors Alliance, traces how courts have treated these uses, from the Turnitin case (A.V. v. iParadigms) through Authors Guild v. Google, Authors Guild v. HathiTrust, and Bartz v. Anthropic, and flags EVOX v. Stanford, a pending suit over a university-hosted image dataset. He then turns to licensing, showing how restrictive terms in academic and consumer contracts can override researchers’ fair use rights, and how publishers now sell AI licenses both to technology companies and to universities. He closes with a third path: Transparent Books, a Mellon-funded partnership of HathiTrust, Northeastern University Library, and Authors Alliance to build secure, noncommercial access to HathiTrust’s digitized books for academic AI and text and data mining research.

Keywords: fair use; copyright; text and data mining; artificial intelligence; AI training data; licensing; open access; research libraries; scholarly communication; HathiTrust; Transparent Books; Public Interest Corpus; Authors Guild v. HathiTrust; Authors Guild v. Google; Bartz v. Anthropic

Related links:

Transparent Books Project FAQ https://www.hathitrust.org/transparent-books-project-faq/ | Authors Alliance https://www.authorsalliance.org/ 

Notes: Presented on Zoom as part of WashU Libraries’ 8th annual Open October. Originally announced as ‘Charting a third path: Fair use, open access, and university-built datasets for academic AI.

Copyright

This work is licensed under CC BY 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/

Recommended Citation

Hansen, Dave, "Charting a Third Path: Fair Use, Open Access, and University-Built Datasets" (2026). Open October. Paper 16.
Retrieved from https://digitalcommons.wustl.edu/openoctober/16