Responsible Operations: Data Science, Machine Learning, and AI in Libraries



 
 


    
     
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            Responsible Operations: Data Science, Machine Learning, and AI in Libraries
    





        
 
               
        
        
        
        
           
        
        
        
          





  
  
    Responsible Operations: Data Science, Machine Learning, and AI in Libraries
 
  by Thomas Padilla
 

  


 







    
    





  
  
    Responsible Operations is intended to help chart library community engagement with data science, machine learning, and artificial intelligence (AI) and was developed in partnership with an advisory group and a landscape group comprised of more than 70 librarians and professionals from universities, libraries, museums, archives, and other organizations.
 
  This research agenda presents an interdependent set of technical, organizational, and social challenges to be addressed en route to library operationalization of data science, machine learning, and AI.
 
  Challenges are organized across seven areas of investigation:
 
  	Committing to Responsible Operations
	Description and Discovery
	Shared Methods and Data
	Machine-Actionable Collections
	Workforce Development
	Data Science Services
	Sustaining Interprofessional and Interdisciplinary Collaboration

 
  Organizations can use Responsible Operations to make a case for addressing challenges, and the recommendations provide an excellent starting place for discussion and action.
 
  Download US Letter .pdf
 
  Download A4 .pdf
 
   
 
   

  


 







       
          





  
  
    Suggested citation:
 
  Padilla, Thomas. 2019. Responsible Operations: Data Science, Machine Learning, and AI in Libraries. Dublin, OH: OCLC Research. https://doi.org/10.25333/xk7z-9g97.

  


 






     


        





    


    
    




       
          





  
  
    

  


 






     


        







  
  
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