Responsible Operations: Data Science, Machine Learning, and AI in Libraries. OCLC Research Position Paper
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| Title: | Responsible Operations: Data Science, Machine Learning, and AI in Libraries. OCLC Research Position Paper |
|---|---|
| Language: | English |
| Authors: | Padilla, Thomas (ORCID |
| Source: | OCLC Online Computer Library Center, Inc. 2019. |
| Availability: | OCLC Online Computer Library Center, Inc. 6565 Kilgour Place, Dublin, OH 43017. Tel: 800-848-5878; Fax: 614-764-6096; e-mail: support@oclc.org; Web site: http://www.oclc.org |
| Peer Reviewed: | N |
| Page Count: | 38 |
| Publication Date: | 2019 |
| Document Type: | Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Data Collection, Data Analysis, Artificial Intelligence, Educational Technology, Technology Uses in Education, Library Role, Man Machine Systems, Information Technology, Bias, Accountability, Electronic Libraries, Academic Libraries, Labor Force Development, Competence, Evidence Based Practice, Library Research, Interdisciplinary Approach |
| ISBN: | 978-1-55653-151-4 |
| Abstract: | 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: (1) Committing to Responsible Operations; (2) Description and Discovery; (3) Shared Methods and Data; (4) Machine-Actionable Collections; (5) Workforce Development; (6) Data Science Services; (7) 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. |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Accession Number: | ED603715 |
| Database: | ERIC |
| Abstract: | 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: (1) Committing to Responsible Operations; (2) Description and Discovery; (3) Shared Methods and Data; (4) Machine-Actionable Collections; (5) Workforce Development; (6) Data Science Services; (7) 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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| ISBN: | 978-1-55653-151-4 |
