found on the web, and this additional information can help contextualize the 
provenance and veracity of information. 
• Breadth, Diversity, and Mitigating Bias:  Books can serve a critical role in ensuring AI 
models are inclusive of a broad range of topics and categories that may be under-
represented in other content. For all that the Internet has generated an explosion in 
human creativity and information sharing, it generally represents only a few decades 
of information and a small portion of the world’s creative population. A books 
dataset, by comparison, is capable of representing centuries of human knowledge. As 
a result such a dataset can help ensure AI systems behavior is based on centuries of 
historical information from modern books. It can help ensure broad geographic and 
linguistic diversity. What’s more, the greater breadth and diversity of high-quality 
content help mitigate challenges around bias and misinformation. Using a more 
diverse pool of training data can help support the production of a model and outputs 
of the model that are more representative of that diversity. Books can be useful in 
evaluation datasets to test existing models for memorization capabilities, which can 
help prevent unintended reproduction of existing works. Of course, this is all 
contingent on actual composition of the corpus; in order to have the bene ﬁts 
described, the books would need to be curated and included with characteristics like 
time, geographic and linguistic diversity.  
• Other Modalities:  Finally, books do not just contain text, they often contain images 
and captions of those images. As such, they can be an important training source for 
multi-modal LLMs, which can receive and generate data in media other than text. 
Lowering Barriers to Entry & Facilitating Competition 
Broad access to books for AI training is critical to ensure powerful AI models are not 
concentrated in the hands of only a few companies. Access to training data, in general, has 
been cited as a potential competitive concern  in the AI ﬁeld because of the performance 11
beneﬁts to be gained by training on larger and larger datasets. But this competitive wedge is 
even more acute when we look speciﬁcally at access to book datasets.  
The largest technology companies building commercial AI models have the resources and 
capacity to mass digitize books for AI training. Google has scanned 40 million books, many 
of which came from digitization partnerships they formed with libraries. They may already 
use some or all of these books to train their AI systems.  It’s unclear to what extent other 12
companies already have acquired books for AI training (for instance, whether Amazon’s 
existing licenses with publishers or self-published authors may permit such uses); 
 See e.g. Trendacosta, Katherine and Doctorow, Cory. “AI Art Generators and the Online Image Market.” 11
Electronic Frontier Foundation, 3 Apr. 2023, www.eff.org/deeplinks/2023/04/ai-art-generators-and-
online-image-market; Narechania, Tejas N., and Sitaraman, Ganesh. “An Antimonopoly Approach to 
Governing Artiﬁcial Intelligence.” SSRN Electronic Journal, 2023, cdn.vanderbilt.edu/vu-URL/wp-content/
uploads/sites/412/2023/10/09151452/Policy-Brief-2023.10.08-.pdf, https://doi.org/10.2139/
ssrn.4597080. Accessed 25 Feb. 2024.
 See white paper for Google’s Gemini models https://arxiv.org/pdf/2312.11805.pdf — “Gemini models 12
are trained on a dataset that is both multimodal and multilingual. Our pretraining dataset uses data from 
web documents, books, and code, and includes image, audio, and video data.”
Towards a Books Data Commons for AI Training 6