Ethical AI and Librarianship
A Resource Guide
Project Overview
Why Ethical AI?
Artificial Intelligence (AI) impacts how people retrieve, select, access, consume, and generate information. Be it personal assistants like Alexa, generative AI tools like ChatGPT, or translation tools like Google Translate, AI have become ubiquitous. The strengths of AI applications have been recognized, such as processing large amounts of data promptly and improving results iteratively, and providing personalized services. While many embraced AI with excitement, ethical concerns of AI have been raised. For example, AI-generated information may be inaccurate, biased, and lack transparency. AI applications may gather and misuse personal data and invade privacy. Among the mix of enthusiastic anticipations and concerned opinions about AI, voices supporting ethical AI surfaced. Ethical AI refers to responsible AI use that complies with established ethical principles. Ethical AI emphasizes the impact of AI on humans. The goal of ethical AI is to optimize the strengths of AI while reducing risks and preventing harm.
Why Libraries? Why Resource Guide?
Libraries, as information organizations that support users with various backgrounds, apply updated technology to user-centered services. Incorporating ethical AI into library services is a critical topic to the library communities. Resources about ethical AI and librarianship have increased rapidly in recent years. Librarians may find it overwhelming to keep track of AI applications, ethics principles and standards, emerging ethical concerns, as well as the impacts of AI on library services. The lack of a resource guide could lead to delayed development of principles and guidelines to support ethical AI in libraries, lack of AI applications to library services, or implementations of AI without thorough consideration of potential ethical implications.
What is this project?
Purpose and Scope
This project aims to address the abovementioned challenges by creating a resource guide on Ethical AI and Librarianship. The guide collects resources published between 2020 and 2024 by international authors and institutions. As the guide is compiled for a wide audience, the team intentionally selected resources of various types (including books, articles, videos, events, and courses/tutorials), and included resources that are free, open, and accessible. In this guide, users can see whether a resource is behind pay wall, and whether caption is available for audio and video resources.
The team recognizes that there are many resource guides developed by libraries and information professional organizations. However, most of the existing guides focus on AI, ethical AI, or AI applications in libraries. This guide emphasizes resources that cover all three topics: libraries, AI, and ethics. A limited number of resources that cover two of the three topics are included to provide a more comprehensive view of ethical AI and librarianship.
Deliverables & Contributions
With the support of the American Library Association and the University of Wisconsin-Milwaukee, this resource guide is freely and openly available. Users can read about the project on this website, examine the metadata schema used to describe the resources, download the PDF file of the resource guide, and download the citations of the resources included in the guide in RDF format. The RDF file can be imported into reference management tools, such as Zotero, for ease of reuse.
This resource guide is relevant to libraries, librarians, library users, and the educators and students in library and information science (LIS). The team expects this guide to assist libraires with making informed decisions on implementing ethical AI in library services, and advance librarians’ understanding of how AI and ethical issues may influence their work. For library users, the guide can inform them on how AI and ethical concerns may shape their library experiences. For LIS educators and students, this guide can support the incorporation of ethical AI into the LIS curriculum, provide information on current trends in AI and ethical concerns, and highlight their influences on careers in library and information science.
Project Team
Wan-Chen Lee (Team lead / Principal investigator)
Dr. Lee is an assistant professor at the University of Wisconsin-Milwaukee School of Information Studies. She received her MLIS, MSIS, and PhD at the University of Washington Information School. Her research interests are knowledge organization, culture and resource description, metadata, and classification theory. Dr. Lee is interested in designing knowledge organization systems for various cultural contexts. She addresses ethical issues, interoperability concerns, and global-local tensions in resource description. Dr. Lee also studies the cultural stewardship of multimedia resources. She conducts research on the metadata for video games, anime, and fiction to support effective description, representation, and retrieval of these resources. Her work has been published in Journal of Documentation, Knowledge Organization, and The Library Quarterly. Recently, she received grants to investigate the challenges and opportunities of AI, as well as ethical AI and librarianship. Her research has been funded by organizations like the American Library Association (ALA) and the Northwestern Mutual Data Science Institute (NMDSI).
Juliana Hirt (Team member / Research assistant)
Juliana is a PhD candidate in the School of Information Studies at the University of Wisconsin-Milwaukee. She holds a Master of Science in Information Science and Technology and a Bachelor of Arts in Mathematics, both from the University of Wisconsin-Milwaukee. Her research interests focus on integrating theories of knowledge organization, information retrieval, and information communication to develop generalizable stochastic models for the optimization of information technology infrastructures. Her current work examines the application of cognitive social constructivist approaches to knowledge organization, with a particular focus on event-driven architecture (EDA) optimization. Juliana’s research has been published in the Journal of Documentation and the proceedings of the Association for Information Science and Technology (ASIS&T) and iConference. She is also involved in research initiatives supported by the American Library Association (ALA) and the Northwestern Mutual Data Science Institute (NMDSI).
Manman Luo (Team member / Research assistant)
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Charlene Chou (Project advisor)
Charlene Chou is the Head of Knowledge Access Department at the New York University Libraries, managing cataloging and metadata services. She has been actively serving on various committees, including the PCC (Program for Cooperative Cataloging) Policy Committee, the RDA Steering Committee, the OCLC RLP Metadata Manager Group, and the Joint RDA Board and RSC Working Group on Artificial Intelligence. She has committed to do pilot projects on emerging trends and technologies. Her research interests lie primarily in the areas of metadata management, the discovery of multilingual resources, artificial intelligence/natural language processing models for subject indexing, digital scholarship, and inclusive metadata.