Researchers who want to apply AI and text-data-mining methods to large collections face a familiar bind: the materials they need are hard to reach, and the two paths on offer both carry costs. Commercial AI developers gather text at scale with little transparency, while publishers increasingly ask universities to license every computational use. This talk makes the case for a third path. It revisits why fair use is a built-in safeguard for research rather than a loophole, drawing on the mass-digitization decisions that settled the point, and situates that doctrine within the federal open-access and information policy now facing legal challenge. It then turns to Transparent Books, a Mellon-funded partnership among HathiTrust, Northeastern, and Authors Alliance that gives academic researchers a community-governed, noncommercial way to build their own datasets for AI work grounded in scholarly values.

Free and open to all, registration is encouraged. A Zoom link will be sent to registered attendees before the session.

For more information, view the event flyer and webpage.

Questions?
Contact Micah Zeller at mdzeller@wustl.edu.