These resources support TAI’s mission and work. Resources are organized to follow the sections of the TAI Toolkit, and include materials created by TAI, sources cited in the Toolkit, and further reading. A general resources section offers broader context on the issues of trust, authenticity, and archives that inform TAI’s mission.
Resources are currently in English and skew toward the United States in relevance. TAI is international in scope, and we want this list to be as well, we especially welcome suggestions of resources from outside the U.S. This page is updated regularly and is not comprehensive. We encourage suggestions and feedback through our Suggestion Form.
Authentication And Provenance
AI Toolkit: Due Diligence Questions
Context Matters: Building Trust in Digital Content
- Presentation on how content transparency can strengthen trust across the digital ecosystem and how secure, standards-based solutions are being applied across a wide range of real-world settings. Provenance technologies can help verify digital content, support evidentiary integrity, and inform policy solutions for a more secure and trustworthy information environment.
Shared Taxonomies To Describe AI-Generated Or AI-Altered Materials
AI Toolkit: Standards Comparison Grid
- Download PDF
- View webinar presentation
IPTC Photo Metadata Working Group
Program for Cooperative Cataloging (PCC) Standing Committee on Standards
AI4LAM Speech-to-Text Working Group (Transcript Provenance Metadata Elements)
Coalition for Content Provenance and Authenticity (C2PA) (Content Credentials
- Develops open, global technical standards for Provenance and Authenticity
Preserving Intent in Nonfiction Media: A Responsible Approach to AI Enhancement | Topaz Labs
Example tutorial for an image upscaling workflow in AI drawing
Licensing/Rights Considerations
AI Toolkit: Content Licensing Language Templates
WIPO (2019). WIPO Technology Trends 2019: Artificial Intelligence. Geneva: World Intellectual Property Organization. (International)
- World Intellectual Property Organization intends for this report to be a factual basis for policy discussions about innovation in AI. Based on a detailed study of patents and other information, it aims to shed light on the trends in AI since the field first developed in the 1950s. Research has involved the analysis of patent data related to AI inventions, as well as data on scientific publications, litigation filings and acquisition activity. These data findings are discussed in detail and accompanied by commentary and industry perspectives from more than 20 of the world’s leading experts in AI
- Other WIPO resources (Intellectual Property and Frontier Technologies)(2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (United States of America))
U.S. Copyright Office: AI report (United States of America)
Engaging With AI Tech Companies
AI Toolkit: Strategic Engagement
User Stories
Other Resources
Content Authenticity and Provenance in the Age of Artificial Intelligence: A Call-to-Action for the LAMs Community
- Released in February 2026 as a product of the C2PA for G+LAM Community of Practice, the white paper advocates for libraries, archives, and museums (LAMs) to take proactive and pragmatic steps to ensure that digital collections content, especially content impacted by AI at any point in its lifecycle, remains authentic, transparent, and verifiable from creation through access in order to meet the LAMs community’s mission of public trust.
University of Virginia Archival AI Protocol
- A practical approach for evaluating AI requests involving cultural heritage collections. It provides AI training and access standards for archival organizations and is organized around a simple rule: “Irreversible models do not get access unless item-level provenance and meaningful attribution can be demonstrated in practice, and the archival organization retains contractually enforceable control to stop further use.”
AI Literacy
Stanford Artificial Intelligence Index Report (2025) (2026)
- Data in layman’s terms to guide the understanding of the AI industry
Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI By Karen Hao ISBN 978-0-59365-750-8
- New York Times Best Seller deep dive into the organizational foundations and goals of main AI companies and specifically OpenAI (ChatGPT, backbone to Microsoft’s AI initiatives)
World Privacy Forum (International)
- publishes reports, data, and provides other resources on data privacy such as their recent Privacy, Identity and Trust in C2PA report and Global Table of National AI Strategies (Regions, Sectors, Governance, and Privacy)
LibGuides
George Mason University LibGuide
Syracuse University Libraries LibGuide
- section on librarianship
ECU Libraries LibGuide: A Guide to Artificial Intelligence (AI) for Students: Environmental Impacts
AI & Machine Learning Risks in Cybersecurity via University of Tennessee Knoxville
AI Use/Restrictions and Challenges in Archives
Patricia C. Franks, “Records and Information Management,” 3rd ed. (Chicago: ALA-Neal Schuman, 2025). 584 pp. ISBN 979-8-89255-588-3.
- Seminal text updated to reference AI and machine learning (ML) in all fourteen chapters of the book as well as 2 new chapters on the subject
The University of Virginia Archival AI Protocol
Library and Archives 101: AI and the False Promise of Control
- Written in response to the UVA Protocols
The Crisis Is Not Data. It Is Context.
- Written in response to the UVA Protocols
Larson, Mary. 2026. “Oral History Encounters AI: An Exploration of Core Principles and Best Practices, Context and Consent.” The Oral History Review 53 (1): 93–110. doi:10.1080/00940798.2026.2625663.
American Archivist Generative AI Statement
- Provides language that may be useful for creating parameters around AI use
Rockefeller Archive Center Documentation: AI Use in Our Archival Processes
- document outlines the reasons why RAC doesn’t don’t currently use AI in archival processes – including archival appraisal, acquisition, arrangement and description (processing), digital preservation, and access – as well as how RAC supports user communities who want to employ AI or other machine-assisted research methodologies in the course of their work. It does not address any potential use of AI in RAC business processes, and it is not intended to be an organizational AI use policy.
Padilla, Thomas. 2019. Responsible Operations: Data Science, Machine Learning, and AI in Libraries. Dublin, OH: OCLC Research.
- 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
Amanda Barajas, “AI in Archives: Reexamining Roles Between Archivist and Machine,” WOVEN: An Interdisciplinary Journal of Dietrich College 7, Fall (2025): 57-72.
- Systematic literature review examining the current literature surrounding AI applications in archival spaces; interviews with archivists
Sonia Yaco, Bala Desinghu, Claire Warwick, and Richard Anderson, “What Can AI Do for Special Collections?,” American Archivist 88, no. 2 (2025): 441-473,
- in-depth analysis of their experiences with specific AI tools applied to untranscribed documents (handwritten and type-written) and photographs. The evaluated tools range from end-user generative AI to APIs requiring Python programming and include software that is commercial and open-source; fee-based and free; web browser-based and standalone; and run in desktop, mobile device, and high performance computing clusters environments. Application scenarios extract text from images, perform complex pattern matching, and generate metadata.
International Research on Permanent Authentic Records in Electronic Systems (InterPARES) Trust AI Research Studies
- Multinational interdisciplinary project to identify AI technologies useful to archives, determine their benefits and risks, and ensure that archival concepts and principles inform the development of responsible AI, including through research