Responsible AI in Libraries and Archives
An IMLS-funded project aiming to support ethical decision-making for AI projects in libraries and archives.
Responsible AI in Libraries and Archives (2022-2025) produces tools and strategies that support responsible use of AI in the field. AI projects in libraries and archives can support increased impact and new uses of resources, and Responsible AI provides new data, new resources, and new strategies that will prepare our profession for the methodical consideration of potential harms of AI projects. As we begin to ethically and responsibly operationalize AI in libraries and archives, we hope that libraries and archives can provide models that resonate beyond the profession, embodying Library of Congress Director of Digital Strategy Kate Zwaard’s idea that “through the slow and careful adoption of tech, the library can be a leader.”
Table of Contents
Project Updates
February 2025: Viewfinder: a toolkit for values-driven AI in libraries and archives (v7)
We have finalized the contents of the toolkit and named it Viewfinder. We are currently working with designer Ananda Gabo to create a visual identity for the toolkit.
Download prototype version 7 via OSF.
Stay tuned for the final tool in Summer 2025!
October 2024: Tool prototype v4
We are nearing the end of our Responsible AI in Libraries and Archives tool design process! From now until the end of the year, we will testing version 3 of the tool. Testing groups include our project advisory board, community experts, and the authors of the case studies that were published in the JESLIB March 2024 special issue.
September 2024: Now published! Responsible AI literature review in Information Technology and Libraries special issue
The Responsible AI in Libraries and Archives team has published a responsible AI literature review in a special issue of Information Technology and Libraries, with the associated dataset shared in Qualitative Data Repository.
Our paper is an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives?
Our literature review finds that just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop.
We ulimately conclude that while relatively few of the currently published articles describing AI projects in libraries and archives discuss ethics, these types of are on the rise. We expect subsequent discussions of relevant ethics and values to increase as well, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access.
- Responsible AI Practice in Libraries and Archives
- Dataset in Qualitative Data Repository
- Full ITAL special issue
August 2024: Initial tool prototype testing at DLF Forum
Scott Young, Bonnie Sheehey, and Sara Mannheimer traveled to DLF Forum in East Lansing, Michigan to test the first prototype of our Responsible AI tool. The tool takes the form of a card deck that includes cards representing library values, potential stakeholders, and sample scenarios. The tool is based on an analysis of the literature and the visioning workshops conducted in early 2024.
This initial version of the tool will be revised according to feedback from the DLF community, and an additional round of testing will be conducted with the Responsible AI advisory board, community experts, and case study authors from the JeSLIB special issue.
March 2024: Now published! Journal of eScience Librarianship Special Issue: Responsible AI in Libraries and Archives
The Journal of eScience Librarianship (JeSLIB) and the Responsible AI in Libraries and Archives project are proud to publish a Special Issue on Responsible AI in Libraries and Archives.
Librarians and archivists are often early adopters and experimenters with new technologies. Our field is also interested in critically engaging with technology, and we are well-positioned to be leaders in the slow and careful consideration of new technologies. Therefore, as librarians and archivists begin using artificial intelligence (AI) to enhance library services, we also aim to interrogate the ethical issues that arise. The IMLS-funded Responsible AI in Libraries and Archives project aims to create resources that will help practitioners make ethical decisions when implementing AI in their work. The case studies in this special issue are one such resource.
Guest editors: Sara Mannheimer, Doralyn Rossmann, Jason Clark, Yasmeen Shorish, Natalie Bond, Hannah Scates Kettler, Bonnie Sheehey, and Scott W. H. Young
The full issue is available at https://publishing.escholarship.umassmed.edu/jeslib/issue/59/info.
September 2023: JeSLIB special issue announcement, visioning workshops
We are excited to announce that we will be partnering with the Journal of eScience Librarianship to publish a Spring 2024 special issue featuring our responsible AI case studies. The issue will also include an in-depth literature review of AI projects in libraries and archives, focusing on how ethical issues are addressed in the literature.
During Fall 2023 and Spring 2024, we will also host a series of Responsible AI visioning workshops — some with librarians & archivists, and others with library users. These workshops will inform our ethical decision-making tool by helping us understand the ethical issues facing practitioners and users.
August 2023: Welcome, Natalie Bond!
Natalie Bond, the Government Information Librarian & Head of Information and User Services at University of Montana, has joined the grant as a co-Investigator. Natalie will provide a reference and instruction perspective.
March 2023: Case studies in progress
The Responsible AI team is working with a cohort of eight author groups to create case studies exploring AI-related ethical challenges in libraries and archives. These case studies will aid in the development of an ethical decision-making tool.
The case studies will be formatted according to the template below.
Responsible AI Case Study Template
Please use simple, accessible language to describe your case study, organizing your narrative according to the following six prompts. Case studies should be no longer than 10 pages (text of no more than 5,000 words).
1. Summary
Provide a 4-5 sentence summary of the project.
2. Project details
Describe what you did;
- who was involved
- what their role(s) were;
- what services were drawn upon;
- what collections were involved and why were they selected;
- what infrastructure and technologies were selected and why;
- what challenges were encountered in the course of the implementation?
3. Background
Describe why the decision was made to implement (or why there was an attempt to implement) the AI project/tools in your library and/or archive.
- What are the benefits of the project or tool?
- What gaps or problems are you trying to address?
- What other tools or projects did you look to for guidance or inspiration?
4. Ethical considerations
Describe any ethical issues that arose as you implemented the project, potentially including:
- How you considered and addressed potential harms associated with the implementation.
- Privacy considerations
- Do users consent to data sharing and use?
- Did you engage with any stakeholders?
- Did you refer to existing documentation, policy, or best practices?
- Did you refer to the ALA Code of Ethics or other codes?
- Did you conduct risk-benefit analyses?
- Did you hear concerns from your user community or your library?
- Include any considerations that are considered unresolved.
5. Who is affected by this project?
Describe the people, services, and programs that are involved in, support the use of, or are affected by the AI implementation (e.g. digital scholarship services, metadata services, subject area liaisons, library users, etc.)
6. Lessons learned and future work
Describe any lessons learned regarding the ethical and responsible implementation and use of AI. What recommendations would you give to others pursuing similar work? Include here any unanswered questions that merit further investigation, and/or any plans to build on the work described in the case study.
7. Documentation
Include any formal documentation (personas, use cases, code samples, functional requirements, privacy policies) or workflows that support the implementation.
Responsible AI Project Overview
Project Timeline
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Phase
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Dates
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Activities
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Deliverables & Dissemination
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Phase 1. Environmental
Scan |
Aug 2022 - |
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Phase 2.Case studies&beneficiary |
Mar 2023 - Sep
2023 |
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Phase 3a. Target group |
Oct
2023 - Mar 2024 |
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Phase 3b. Ethically-relevant
Harms Analysis Tool Assessment and Validation |
Apr 2024 - Oct
2024 |
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Phase 4. Final Ethically- |
Nov 2024 - July
2025 |
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Research Outputs
Responsible AI special issue of the Journal of eScience Librarianship
Other research products available via OSF https://doi.org/10.17605/OSF.IO/RE2X7
Project Team
Project Director
- Sara Mannheimer is Data Librarian at Montana State University (MSU). She has experience teaching and and conducting computational data analysis using the R programming language. Her research focuses on quantitative and qualitative social science research methods and applied ethics. She is affiliated faculty in the MSU Center for Science, Technology, Ethics, and Society and serves as project manager for the MSU Dataset Search.
Co-Project Directors
- Jason Clark is head of the Research Optimization, Analytics, and Data Services department at MSU Library. He is a technologist and software application developer (PHP and Python) whose work focuses on innovations in library software, including AI applications such as speech-to-search and anticipatory design search. Clark was PD of the IMLS-funded Algorithmic Awareness project, which created teaching resources to support understanding of the practical and ethical implications of algorithms, including algorithms for AI.
- Doralyn Rossmann is Dean of the Library at MSU. Her background is in information technology in higher education and libraries. Rossmann has managed IT systems and software development at several universities and has served on MSU’s Senior Information Technology Leadership Team. She has expertise in writing privacy policies and management of user data in libraries. Her research focuses on social network data ethics and augmented and virtual realities.
- Bonnie Sheehey is an ethicist in the MSU Department of History and Philosophy. Sheehey focuses on the social, ethical, and political implications of technology. She has published on issues of racial bias and injustice in current technologies deployed in a variety of criminal justice practices in the U.S. context. Sheehey is affiliated faculty in the MSU Center for Science, Technology, Ethics, and Society.
- Scott W. H. Young is User Experience and Assessment Librarian at MSU. He is a library service designer and has experience facilitating multi-day participatory design workshops similar to that being proposed above. He served as PD of the IMLS-funded National Forum on Web Privacy and Web Analytics (co-PDs Mannheimer and Clark), which featured a 2.5-day forum thatfunctioned as a participatory design workshop in support of privacy-oriented library services. His research focuses on applying library values through the lens of practical ethics. Young is affiliated faculty in the MSU Center for Science, Technology, Ethics, and Society.
- Natalie Bond is Government Information Librarian & Head of Information and User Services at University of Montana. She is interested in human rights and social justice as they relate to archives and library services.
- Yasmeen Shorish is Director of Scholarly Communications Strategies at James Madison University. Her work focuses on data ethics and privacy. With expertise in data librarianship and scholarly communications, Yasmeen has a unique perspective on how data products and computing intersect with the scholarly record. She has presented and led workshops on the threats posed by digital surveillance on privacy and society. She has also investigated issues related to data discoverability in her leadership on the IMLS-funded OA in the Open and ethical means of computational analysis of library collections in her involvement with the Mellon-funded Collections as Data: Part to Whole project.
- Hannah Scates Kettler is Associate University Librarian for Academic Services at Iowa State University. Her work focuses on creating and highlighting intervention points in library work that invite critical assessment of approach and outcomes. Through her work in the DLF Cultural Assessment Working Group, she has co-developed intervention points for selection and appraisal of digital collection creation and preservation and instills those concepts at the local level. She has worked on the development of community driven 3D data preservation standards, shepherding digital collections as data models which focus on ethical use and reuse through her involvement in the IMLS-funded Collections as Data: Always Already Computationalproject and leadership in the Mellon-funded Collections as Data: Part to Whole projects.
Advisory Board
- Dorothy Berry, Smithsonian Institution
- Stephanie Russo Carroll, University of Arizona
- Bohyun Kim, University of Michigan
- María Matienzo, University of California, Berkeley
- Thomas Padilla, Bristlecone Strategy
Project Sponsors
This project is made possible in part by the Institute of Museum and Library Services, through grant # LG-252307-OLS-22. The IMLS is the primary source of federal support for the nation’s libraries and museums. They advance, support, and empower America’s museums, libraries, and related organizations through grantmaking, research, and policy development. Their vision is a nation where museums and libraries work together to transform the lives of individuals and communities. To learn more, visit www.imls.gov.