content repositories, like libraries, with that of AI developers. A “books data commons” needs 
to be both responsibly managed, and useful for developers of AI models. 
We use “commons” here in the sense of a resource that is broadly shared and accessible, 
and thus obviates the need for each individual actor to acquire, digitize, and format their own 
corpus of books for AI training. This resource could be collectively and intentionally 
managed, though we do not mean to select a particular form of governance in this paper.   4
This paper is descriptive, rather than prescriptive, mapping possible paths to building a 
books data commons as de ﬁned above and key questions relevant to developers, 
repositories, and other stakeholders, building on our workshop discussions. We ﬁrst explain 
why books matter for AI training and how broader access could be bene ﬁcial. We then 
summarize two tracks that might be considered for developing such a resource, highlighting 
existing projects that help foreground both the potential and challenges. Finally, we present 
several key design choices, and next steps that could advance further development of this 
approach.  5
 In this way, we do not use “commons” in the narrow sense of permissively licensed. What’s more, this 4
resource could also be governed as more of a data “trust,” and, indeed, we discuss extensively the work 
of HathiTrust as a relevant project in this domain. However, our use of the word “commons” is not 
meant to preclude this or other arrangements.
 There are, of course, a range of other types of texts that are not on the web and/or not digital at all - 5
e.g., periodicals, journals, government documents. These are out of scope for this paper, but also worthy 
of further analysis.
Towards a Books Data Commons for AI Training 2