Ethical AI and Librarianship

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Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project

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    		Description
    Title Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project
    Type Reports
    Creator Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack
    Link
    Creation Date 01/10/2020
    Last Updated Date 06/15/2020
    Summary Prepared by Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, and Chulwoo Pack from University of Nebraska–Lincoln and submitted to the Library of Congress, this final report presents findings from a 2019–2020 demonstration project titled “Digital Libraries, Intelligent Data Analytics, and Augmented Description”. The project explored how machine learning (ML) and image processing can support metadata generation, access, and discovery in large-scale digital library collections in cultural heritage institutions. The report includes: 1) six exploratory experiments focusing on two Library of Congress collections: Chronicling America (digitized historical newspapers) and By the People (crowdsourced manuscript transcriptions) using image processing and machine learning techniques. 2) Discussion of social, technical, and socio-technical challenges encountered during the project. 3) Recommendations for the Library of Congress's future machine learning efforts. 4) Code and data used for the project, see Link field. Section 7 (Discussion) highlights ethical risks and considerations when deploying ML in cultural heritage contexts:
    • Cultural heritage institutions must critically examine machine learning's epistemological foundations, specifically how historically biased collecting and description practices can reinforce systemic biases in training data.
    • Effective machine learning requires integrated thinking that considers both technical capabilities and social implications together, rather than treating them as separate domains.
    Examples of ethical recommendations for the Library of Congress (see section 8) include:
    • The Library should prioritize building social and technical infrastructure to support ML development in cultural heritage settings. This includes creating a statement of values and a machine learning roadmap aligned with institutional goals and needs of larger cultural heritage communities.
    • The Library should sponsor competitions where teams use ML to generate metadata for its digital collections. These challenges would advance metadata design, encourage critical engagement with ethical and technical issues (e.g., bias), and promote interdisciplinary collaboration required for responsible ML development.
    Topic Ethical AI. AI and Librarianship. Digital Collection.
    Source and Link Library of Congress Labs. https://labs.loc.gov/
    Access Open
    Accessibility --
    Audience Librarians – Academic (& Research). Scholars and Students. Information Professionals.
    Platform or Format Document (.pdf)
    Length 46 pages
    Geography USA
    Language ENG
    Description Date 06/18/2025

    Ethical AI and Librarianship: A Resource Guide

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