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.

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.

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.

Tech companies increasingly seek what archives hold—rich media collections to train their multimodal and large language models. 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, the Strategic Engagement Working Group is developing decision-making rubrics to help institutions assess collaboration opportunities with tech companies. 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.