Author Archive: trustarchives

Events

September 21-24
ATALM | Spokane, Washington

MEETING THE MOMENT: ADDRESSING THE IMPACT OF GENERATIVE AI ON ARCHIVAL MEDIA COLLECTIONS

 

July 29 – Aug 1
SAA | New Orleans, LA

ARCHIVES AND AI: MEETING THE CHALLENGES AND OPPORTUNITIES OF A TECHNOLOGICAL REVOLUTION

 

April 8
EUscreen Symposium| Warsaw, Poland

REFRAMING OPENNESS – EMPOWERING AUTHENTICITY AND REUSE IN AUDIOVISUAL HERITAGE
Fabio Paul Bedoya Huerta

 

 

2025

December 4
AMIA |  Baltimore, MD

TRUST IN ARCHIVES: ARCHIVES IN THE AGE OF AI
Charlie Eckert, Rachel Antell, Phillip Sulentic, Fabio Paul Bedoya Huerta

AI TOOLS FOR ARCHIVES

Generative artificial intelligence is rapidly reshaping how audiovisual materials are created, reused, and interpreted. For archives, libraries, and cultural heritage organizations, these changes raise urgent questions about authenticity, rights, access, and the responsible use of archival materials.

This set of tools, developed by the Working Groups of the Trust in Archives Initiative, is designed to help archives navigate this evolving landscape. The toolkit provides practical guidance on issues including authentication and provenance, licensing considerations, working with technology companies, and the development of shared taxonomies to describe AI-generated or AI-altered materials.

Because both AI technologies and archival practices continue to evolve, these tools are intended as a living resource. They will be updated and expanded over time, and feedback from the community is welcomed to help inform future revisions and the development of additional tools.

 

 

 

Tools: Taxonomies

 

With GenAI now pervasive across media creation, archives need a shared language to guide their policies and to frame the implications for collections. Questions arise, for example, about when upscaling stops being a matter of improving quality and instead constitutes the creation of a new work, or how best to describe materials that are AI-generated or AI-altered in consistent and transparent terms.

TAI’s Taxonomies Working Group is addressing these challenges by gathering glossaries from across the field, analyzing existing frameworks, and identifying where gaps remain. Our goal is to create a comprehensive, field-wide metaglossary that enables archivists to clearly understand, evaluate, and communicate the range of machine-learning processes and their impact on archival media.

This AI Taxonomies tool will continue to evolve. Please add your comments or contact us at info@trustarchives.org to contribute to this work.

 

 

 

TAI Tools: Content Licensing

These templates are designed to ensure the preservation, integrity, and authenticity of the archival content. In cases where Artificial Intelligence (AI) manipulation is used, the template helps ensure that these changes are flagged, and a review is conducted to ensure compliance with standards established by the Archive.

Consideration was given to the fact that many standard post-production techniques (including but not limited to color correction, image stabilization, dirt and scratch reduction, etc.) are now being performed by assistive AI software. In many cases, Licensees may be unable to easily report whether the software used in post-production is “AI powered” or not.

To address a wide range of use cases, the Trust in Archives Initiative (TAI) Content Licensing Working Group has developed two license templates. These are designed to reflect the differing needs of production and publication workflows, ranging from film and documentary projects to print and digital publications.

For those Archives that may allow for
limited use of AI on their materials

For those Archives that may wish to outright
prohibit any use of AI on their materials

TAI Tools: Authentication

 

DUE DILIGENCE QUESTIONS TO ESTABLISH ARCHIVAL AUTHENTICITY

As AI-generated media becomes increasingly commonplace, it is more important than ever for archives to know and be assured of the provenance and authenticity of the records they take into their custody.* There are technological methods being developed to detect AI-generation or modification and to convey or disclose provenance information. However, these mechanisms may remain out of reach for many content producers and smaller archives– at least in the short-term– due to financial and personnel limitations. There is still a need for accessible approaches for examining provenance and verifying authenticity in the age of generative AI that do not require specialized tools. To this end, the Authenticity Working Group is developing a simple checklist of questions that archives can ask depositors as part of their due diligence in ascertaining the reliability of the records they accession. Archives can retain the answers to these questions for their records and develop their own systems to share this data with other institutions they work with.

Archival producers and media makers are similarly concerned about the authenticity of the media they obtain from archives for use in documentaries. The second section contains questions that producers can use when working with archives, or that archives can answer for themselves to attest to the authenticity of materials already in their collections.

 

USER STORIES

Over the past year, conversations within the TIA Authenticity working group have revealed that the exponential growth of generative AI is affecting archives, archivists, and their moving image collections. Inspired by the Library of Congress’ user stories for C2PA implementation in Government and Libraries, Archives, and Museums, the TIA Authenticity working group asked AMIA members to submit user stories to help us better understand specific concerns regarding GenAI as they relate to different roles and types of archives within the community. The stories below come from Authenticity Working Group members and from participants at our 2025 AMIA conference. We continue to seek and welcome new submissions. These user stories offer insightful perspectives that help us create tools and resources that will further our mission of ensuring authenticity, transparency, and trust.

 

 

*Provenance refers to the origins, custody, and ownership history of a record. A record’s provenance is key to its intelligibility and significance as documentary evidence. Authenticity, a closely related concept, refers to the record being genuine and free from tampering. A record’s authenticity enables it to be relied upon as evidence or proof of what it documents.

TAI Tools: Strategic Engagement with Tech Companies

AI Technology Companies (“AI Tech Co”) increasingly seek what archives hold—rich media collections to train their multimodal and large language models (“AI”). Many archives have already had their online collections scraped without permission and are now facing offers from companies eager to secure further access.

To support archives navigating these pressures, we are developing decision-making rubrics to help institutions assess collaboration opportunities with AI Tech Cos.

These tools are designed to guide archives in weighing whether such deals represent sustainable business opportunities that strengthen their futures—or risky bargains that could compromise their long-term best interests. This paper serves as part of that tool, and is intended to help as a comprehensive resource for guidance on some key considerations and critical questions to ask.

The document covers:
• What is Generative AI?
• What type of companies want to obtain your data?
• 10 Key Things to Consider when looking at AI Tech Companies and why this is important
• Five more key areas to think about (introspective)
• Your Internal Technical Preparation
• Questions to ask the AI Tech Co.

 

Licensing

As new technologies expand the ability to reuse, transform, and even synthetically create archival materials, clear licensing language has become essential—not only to protect collections, set boundaries for use, and ensure alignment with institutional values, but also to safeguard the authenticity of the materials themselves.

To meet this need, the Licensing Language Working Group is developing adaptable boilerplate language that archives can use when updating license agreements to address generative AI. These tools are intended as a practical framework, helping institutions safeguard authenticity, protect collections, and ensure their policies reflect both mission and values while responding thoughtfully to emerging technological capabilities.

Authentication

As AI-generated media becomes increasingly prevalent, archives face mounting pressure to ensure the provenance and authenticity of the records they steward. While emerging technologies can help detect AI-generation or modification and disclose provenance information, many of these mechanisms remain costly, complex, or limited in reliability, particularly for smaller archives with constrained resources.

The Authenticity Working Group is developing a rage of tools and recommendations to address these challenges. Our goal is to help archives of all sizes evaluate available options and implement the most effective methods of authentication and attestation for their unique contexts, ensuring that collections remain trusted sources of the historical record.

Taxonomies

With GenAI now pervasive across media creation, archives need a shared language to guide their policies and to frame the implications for collections. Questions arise, for example, about when upscaling stops being a matter of improving quality and instead constitutes the creation of a new work, or how best to describe materials that are AI-generated or AI-altered in consistent and transparent terms.

The Taxonomies Working Group is addressing these challenges by gathering glossaries from across the field, analyzing existing frameworks, and identifying where gaps remain. Our goal is to create a comprehensive, field-wide metaglossary that enables archivists to clearly understand, evaluate, and communicate the range of machine-learning processes and their impact on archival media.