text: 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. Speciﬁcally, 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 aﬃrmatively compel 
  sharing access to these digital book ﬁles for AI training. 