Concern 740 · Working draft · editorial review in progress
AI-generated content contaminating archives
AI contamination of archives occurs when generated or altered material enters a collection without a clear record of its origin and changes.
Understanding the concern
Archives may collect born-digital records, images and descriptions. AI can create realistic material or alter an existing item while leaving little visible trace. It can also generate catalogue text that adds unsupported details. Future users may then treat a modern invention as a record of something that actually happened.
Library and archive guidance emphasizes documenting provenance: where an item came from and what happened to it. Think of a museum label becoming separated from the object it describes. Keeping originals, recording edits and distinguishing generated descriptions from source material help preserve trust. A provenance record supports checking but does not automatically make every claim true.
An example
An AI-enhanced image is stored without the original or a note describing which details were generated.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Provenance
- The history of information: where it came from and how it has been handled or changed.
Evidence and its limits
The guidance identifies preservation practices and risks. It does not establish how much synthetic material is already present in all archives.
Library of Congress and C2PA for G+LAM community 2026 · Cultural-heritage practice guidance
Content Authenticity and Provenance in the Age of Artificial Intelligence: A Call to Action for the Libraries, Archives and Museums Community ↗ (opens in a new tab)Documenting origin and changes across digital collection lifecycles. Provenance supports verification but does not independently prove every factual claim.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can a future researcher distinguish the original record from later automated additions and edits?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Learning together
Learning together means treating experience as something a group can examine and improve. It values honest feedback over appearing successful.
Agree on a question, try something manageable, and compare what different people observed. Share failures as well as gains. Decide together what the evidence supports and what remains uncertain.
Ask together: What have we learned together that should guide the next step?
Humility
Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.
State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.
Ask together: What might we be missing, and who could help us see it?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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