JOURNAL ARTICLE

Assessing Artificial Intelligence for Ethical Use in Libraries, Archives, and Museums.

  • Published In: Collections: A Journal for Museum & Archives Professionals, 2026, v. 22, n. 2. P. 101 1 of 3

  • Database: Library & Information Science Source 2 of 3

  • Authored By: Baucom, Erin 3 of 3

Abstract

This article focuses on guiding libraries, archives, and museums (LAMs) in identifying and ethically implementing artificial intelligence (AI) solutions to enhance workflows and resource accessibility. It defines key AI concepts, including machine learning, deep learning, computer vision, and natural language processing, and outlines practical use cases such as automated transcription, metadata generation, and privacy-sensitive data identification. The article emphasizes the importance of evaluating AI models for ethical considerations by examining training data, the diversity of development teams, and privacy implications, urging transparency and accountability in AI use. It also discusses options for adopting AI through open-source tools or vendor-provided solutions, highlighting the need for careful scrutiny of contracts and vendor practices to protect patron privacy.

Additional Information

  • Source:Collections: A Journal for Museum & Archives Professionals. 2026/06, Vol. 22, Issue 2, p101
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2026
  • ISSN:15501906
  • DOI:10.1177/15501906261439246
  • Accession Number:193622706
  • Copyright Statement:Copyright of Collections: A Journal for Museum & Archives Professionals is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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