1. Introduction  1
While the ﬁeld of arti ﬁcial intelligence research and technology has a long history, broad 
public attention grew over the last year in light of the wide availability of new generative AI 
systems, including large language models (LLMs) like GPT-4, Claude, and LLaMA-2. These 
tools are developed using machine learning and other techniques that analyze large datasets 
of written text, and they are capable of generating text in response to a user’s prompts. 
While many large language models rely on website text for training, books have also played 
an important role in developing and improving AI systems. Despite the widespread use of e-
books and growth of sales in that market, books remain di ﬃcult for researchers and 
entrepreneurs to access at scale in digital form for the purposes of training AI. 
In 2023, multiple news publications reported on the availability and use of a dataset of books 
called “Books3” to train LLMs.   The Books3 dataset contains text from over 170,000 books, 2
which are a mix of in-copyright and out-of-copyright works. It is believed to have been 
originally sourced from a website that was not authorized to distribute all of the works 
contained in the dataset. In lawsuits brought against OpenAI, Microsoft, Meta, and 
Bloomberg related to their LLMs, the use of Books3 as training data was speciﬁcally cited.   3
The Books3 controversy highlights a critical question at the heart of generative AI: what role 
do books play in training AI models, and how might digitized books be made widely 
accessible for the purposes of training AI? What dataset of books could be constructed and 
under what circumstances?  
In February 2024, Creative Commons, Open Future and Proteus Strategies convened a series 
of workshops to investigate the concept of a responsibly designed, broadly accessible 
dataset of digitized books to be used in training AI models. Conducted under the Chatham 
House Rule, we set out to ask if there is a possible future in which a “books data commons 
for AI training” might exist, and what such a commons might look like. The workshops 
brought together practitioners on the front lines of building next-generation AI models, as 
well as legal and policy scholars with expertise in the copyright and licensing challenges 
surrounding digitized books. Our goal was also to bridge the perspective of stewards of 
 Authored by Alek Tarkowski and Paul Keller (Open Future), Derek Slater and Betsy Masiello (Proteus 1
Strategies) in collaboration with Creative Commons. We are grateful to participants in the workshops, 
including Luis Villa, Tidelift and openml.fyi; Jonathan Band; Peter Brantley, UC Davis; Aaron Gokaslan, 
Cornell; Lila Bailey, Internet Archive; Jennifer Vinopal, HathiTrust Digital Library; Jennie Rose Halperin, 
Library Futures/NYU Engelberg Center, Nicholas P . Garcia, Public Knowledge; Sayeed Choudhury; Erik 
Stallman, UC Berkeley School of Law. The paper represents the views of the authors, however, and 
should not be attributed to the workshop as a whole. All mistakes or errors are the authors’.
 See e.g. Knibbs, Kate. “The Battle over Books3 Could Change AI Forever.” Wired, 4 Sept. 2023, 2
www.wired.com/story/battle-over-books3/.
 For key documents in these cases, see the helpful compendium at “Master List of Lawsuits v. AI, 3
ChatGPT, OpenAI, Microsoft, Meta, Midjourney & Other AI Cos.” Chat GPT Is Eating the World, 27 Dec. 
2023, chatgptiseatingtheworld.com/2023/12/27/master-list-of-lawsuits-v-ai-chatgpt-openai-microsoft-
meta-midjourney-other-ai-cos. See also “Fair Use Week 2024: Day Two with Guest Expert Brandon 
Butler.” Fair Use Week, sites.harvard.edu/fair-use-week/2024/02/26/fair-use-week-2024-day-two-with-
guest-expert-brandon-butler/. Accessed 20 Mar. 2024 (arguing that use of this dataset is not 
consequential for the fair use analysis).
Towards a Books Data Commons for AI Training 1