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