engagement. And, at least in the U.S., it could generate billions of dollars in damages if the 
speciﬁc design choices and technical constraints are not adequate to justify a ﬁnding of fair 
use. 
This sort of books dataset could be built by expanding use of in-copyright books that have 
already been digitized from existing libraries and other sources. Speci ﬁcally, workshop 
participants mentioned that the Internet Archive, HathiTrust, and Google as entities that have 
digitized books and could repurpose their use to build a books commons, although 
challenges with using these datasets were noted. The Internet Archive is in the midst of 
litigation brought by book publishers for its program for lending digital books; while not 
directly relevant to the issue of AI training using their corpus of books, this sort of litigation 
creates a chilling effect on organizations seeking to make new uses of these digitized books. 
Meanwhile, Google encumbered HathiTrust’s digital copies with certain contractual 
restrictions, which would need to be addressed to develop a books dataset for AI training, 
and Google itself is unlikely to share its own copies while it provides them a competitive 
advantage. 
Perhaps as a matter of public policy, these existing copies could be made more freely 
available. For instance, to ensure robust competition around AI and advance other public 
interests, policymakers could remove legal obstacles to the sharing of digitized book ﬁles for 
use in AI training. Alternatively, policymakers could go further and a ﬃrmatively compel 
sharing access to these digital book ﬁles for AI training. 
It's possible that there could be a new mass digitization initiative, turning physical books into 
new digital scans. At least in theory, one could try to replicate the existing corpora of 
HathiTrust, for example, without Google’s contractual limitations. At the same time, such an 
effort would take many years, and it seems unlikely that many libraries would want to go to 
the trouble to have their collections digitized a second time. Moreover, while new scans may 
provide some incremental bene ﬁt over use of existing ones (e.g., by using the most modern 
digitization and OCR tools and thus improving accuracy), there is no inherent social value to 
making every entity that wants to do or allow AI training invest in their own redundant 
scanning.  
A new digitization effort could target works that have not been yet digitized. This may be 
particularly useful given that previous book digitization efforts, and the Google Books project 
in particular, have focused heavily (though not exclusively) on libraries in English-speaking 
countries. Additional digitization efforts might make more sense for books in those 
languages that have not yet been digitized at a meaningful scale. Any new digitization effort 
might therefore start with a mapping of the extent to which a books corpus in a given 
language has been digitized.  
Towards a Books Data Commons for AI Training 16