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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 0000-0002-6743-6592), OCLC Research
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
FullText Text:
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  Data: Responsible Operations: Data Science, Machine Learning, and AI in Libraries. OCLC Research Position Paper
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  Data: 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
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  Data: 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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  Data: ED603715
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      – Text: English
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      – SubjectFull: Data Collection
        Type: general
      – SubjectFull: Data Analysis
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Educational Technology
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Man Machine Systems
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      – SubjectFull: Bias
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      – SubjectFull: Accountability
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      – SubjectFull: Electronic Libraries
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      – SubjectFull: Academic Libraries
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