Computational research techniques such as text and data mining (TDM) hold tremendous opportunities for researchers across disciplines ranging from mining scientific articles to create better systematic reviews or curated chemical property datasets to building a corpus of films to understand how concepts of gender, race, and identity are shared over time. Unfortunately, TDM, machine-learning, and AI research is often stifled because of legal uncertainties related to copyright or restrictive terms of use. Recent high-profile copyright lawsuits brought against Microsoft, Github, and StabilityAI underscore the legal complications. For authors and researchers, these issues can mean redirecting their research to avoid legal complications. For universities, legal concerns can have a chilling effect on the scale and speed of research support. And for libraries, TDM and legal issues arise both in licensing content for use and for reuse of their own digitized collections.
The 90-minute workshop will be led by Dave Hansen, Executive Director of Authors Alliance and co-PI of the Text and Data Mining: Demonstrating Fair Use Project, which is generously supported by the Mellon Foundation. He works to support authors on information law and policy issues, with a particular focus on how to help maximize the reach and impact of authors' work to benefit the public. Prior to joining the Authors Alliance, Dave was an Associate University Librarian and Lead for Copyright & Information Policy at Duke University Libraries. He holds a JD and MSLS from UNC Chapel Hill.