An Update on our Text and Data Mining: Demonstrating Fair Use Project

Posted April 28, 2023

Back in December we announced a new Authors Alliance’s project, Text and Data Mining: Demonstrating Fair Use, which is about lowering and overcoming legal barriers for researchers who seek to exercise their fair use rights, specifically within the context of text data mining (“TDM”) research under current regulatory exemptions. We’ve heard from lots of you about the need for support in navigating the law in this area. This post gives a few updates. 

Text and Data Mining Workshops and Consultations

We’ve had a tremendous amount of interest and engagement with our offers to hold hands-on workshops and trainings on the scope of legal rights for TDM research. Already this spring, we’ve been able to hold two workshops in the Research Triangle hosted at Duke University, and a third workshop at Stanford followed by a lively lunch-time discussion. We have several more coming. Our next stop is in a few weeks at the University of Michigan, and we have plans in the works for workshops in the Boston area, New York, a few locations on the West Coast, and potentially others as well. If you are interested in attending or hosting a workshop with TDM researchers, librarians, or other research support staff, please let us know! We’d love to hear from you. The feedback so far has been really encouraging, and we have heard both from current TDM researchers and those for whom the workshops have opened their eyes to new possibilities. 

ACH Webinar: Overcoming Legal Barriers to Text and Data Mining
Join us! In addition to the hands-on in-person workshops on university campuses, we’re also offering online webinars on overcoming legal barriers to text and data mining. Our first is hosted by the Association for Computers and the Humanities on May 15 at 10am PT / 1pm ET. All are welcome to attend, and we’d love to see you online!
Read more and register here. 


A second aspect of our project is to research how the current law can both help and hinder TDM researchers, with specific attention to fair use and the DMCA exemption that Authors Alliance obtained for TDM researchers to break digital locks when building a corpus of digital content such as ebooks or DVDs.

Christian Howard-Sukhil, Authors Alliance Text and Data Mining Legal Fellow

To that end, we’re excited to announce that Christian Howard-Sukhil will be joining Authors Alliance as our Text and Data Mining Legal Fellow. Christian holds a PhD in English Language and Literature from the University of Virginia and is currently pursuing a JD from the UC Berkeley School of Law. Christian has extensive digital humanities and text data mining experience, including in previous roles at UVA and Bucknell University. Her work with Authors Alliance will focus on researching and writing about the ways that current law helps or hinders text and data mining researchers in the real world. 

The research portion of this project is focused on the practical implications of the law and will be based heavily on feedback we hear from TDM researchers. We’ve already had the opportunity to gather some feedback from researchers including through the workshops mentioned above, and plan to do more systematic outreach over the coming months. Again, if you’re working in this field (or want to but can’t because of concerns about legal issues), we’d love to hear from you. 

At this stage we want to share some preliminary observations, based on recent research into these issues (supported by the work of several teams of student clinicians) as well as our recent and ongoing work with TDM researchers:

1) Licenses restrictions are a problem. We’ve heard clearly that licenses and terms of use impose a significant barrier to TDM research. While researchers are able to identify uses that would qualify as fair use and also many uses that likely qualify under the DMCA exemption, terms of use accompanying ebook licenses can override both. These terms vary, from very specific prohibitions–e.g., Amazon’s, which says that users “may not attempt to bypass, modify, defeat, or otherwise circumvent any digital rights management system”–to more general prohibitions on uses that go beyond the specific permissions of the license–e.g., Apple’s terms, which state that “No portion of the Content or Services may be transferred or reproduced in any form or by any means, except as expressly permitted.” Even academic licenses, often negotiated by university libraries to have  more favorable terms, can still impose significant restrictions on reuse for TDM purposes. Although we haven’t heard of aggressive enforcement of those terms to restrict academic uses, even the mere existence of those terms can have chilling and negative real world impacts on research using TDM techniques.

The problem of licenses overriding researchers rights under fair use and other parts of copyright law is of course not limited to just inhibiting text and data mining research. We wrote about the issue, and how easy it is to evade fair use, a few months ago, discussing the many ways that restrictive license terms can inhibit normal, everyday uses of works such as criticism, commentary and quotation. We are currently working on a separate paper documenting the scope and extent of “contractual override,” and will be part of a symposium on the subject in May, hosted by the Association of Research Libraries and the American University, Washington College of Law Program on Information Justice and Intellectual Property.

2) The TDM exemption is flexible, but local interpretation and support can vary. We’ve heard that the current TDM exemption–allowing researchers to break technological protection measures such as DRM on ebooks and CSS on DVDs–is an important tool to facilitate research on modern digital works. And we believe the terms of that exemption are sufficiently flexible to meet the needs of a variety of research applications (how wide a variety remains to be seen through more research). But local understanding and support for researchers using the exemption can vary. 

For example, the exemption requires that the university that the TDM research is associated with implement “effective security measures” to ensure that the corpus of copyrighted works isn’t used for another purpose. The regulation further explains that in the absence of a standard negotiated with content holders, “effective security measures” means “measures that the institution uses to keep its own highly confidential information secure.” University  IT data security standards don’t always use the same language or define their standard to cover “highly confidential information” and so university IT offices must interpret this language and implement the standard in their own local context. This can create confusion about what precisely universities need to do to secure the TDM corpora. 

Some of these definitional issues are likely growing pains–the exemption is still new and universities need time to understand and implement standards to satisfy its terms in a reasonable way–it will be important to explore further where there is confusion on similar terms and how that might best be resolved. 

3) Collaboration and sharing are important. Text and data mining projects are often conceived of as part of a much larger research agenda, with multiple potential research outputs both from the initial inquiry and follow-up studies with a number of researchers, sometimes from a number of institutions. Fair use clearly allows for collaborative TDM work –e.g., in  Authors Guild v. HathiTrust, a foundational fair use case for TDM research in the US, we observe that the entire structure of HathiTrust is a collective of a number of research institutions with shared digital assets. And likewise, the TDM exemption permits a university to provide access to “researchers affiliated with other institutions of higher education solely for purposes of collaboration or replication of the research.” The collaborative aspect of this work raises some challenging questions, both operationally and conceptually. For example, the exemption for breaking digital locks doesn’t define precisely who qualifies as a researcher who is “affiliated,” leaving open questions for universities implementing the regulation. More conceptually, the issue of research collaboration raises questions about how precisely the TDM purpose must be defined when building a corpora under the existing exemption, for example when researchers collaborate but investigate different research questions over time. Finally, the issue of actually sharing copies of the corpus with researchers at other institutions is important because at least in some cases, local computing power is needed to effectively engage with the data. 

Again, just preliminary research, but some interesting and important questions! If you are working in this area in any capacity, we’d love to talk. The easiest way to reach us is at

Want to Learn More?
This current Authors Alliance project is generously supported by the Mellon Foundation, which has also supported a number of other important text and data mining projects. We’ve been fortunate to be part of a broader network of individuals and organizations devoted to lowering legal barriers for TDM researchers. This includes efforts spearheaded by a team at UC Berkeley to produce the “Legal Literacies for Text Data Mining” and its current project to address cross-border TDM research, as well as efforts from the Global Network on Copyright and User Rights, which has (among other things) led efforts on copyright exceptions for TDM globally.