Responsible Operations: Data Science, Machine Learning, and AI in Libraries was written by Thomas Padilla, Practitioner Researcher in Residence at OCLC Research and Interim Head, Knowledge Production at the University of Nevada Las Vegas.
From OCLC Research:
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.
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