What dataset management practices are necessary? 
No matter how a books data commons gets built, it will be important to consider broader 
aspects of data governance. For example: 
• Dataset documentation and transparency: Transparent documentation is important 
for any dataset used for AI training. A datasheet is a standardized form of 
documentation that includes information about provenance and composition of data, 
and includes information on management practices, recommended uses or collection 
process. 
• Quality assurance: Above, we note the many features that make books useful for AI 
training, as compared with web data, for example. That said, the institution managing 
a books commons dataset may still want to collect and curate the collection to meet 
the particular purposes of its users. For instance, it may want to take steps to 
mitigate biases inherent in the dataset, by ensuring books are representative of a 
variety of languages and geographies. 
• Understanding uses: The institution managing a books commons dataset could 
measure and study how the dataset is used, to inform future improvements. Such 
monitoring may also enable accountability measures with respect to uses of the 
dataset. Introducing community norms for disclosing datasets used in AI training and 
other forms of AI research would facilitate such monitoring.  
• Governance mechanisms: In determining matters like acceptable and ethical use, the 
fundamental question is “who decides.” While this might be settled simply by whoever 
sets up and operates the dataset and related infrastructure, participatory 
mechanisms — such as advisory bodies bringing together a broad range of users and 
stakeholders of a collection — could also be incorporated. 
Towards a Books Data Commons for AI Training 19