text: *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. 