Provenance labeling
Stamp unverified and AI-generated records so people and systems down-weight them instead of inheriting them as fact.
What it changes
Who can pull it
What it looks like institutionally
A record system that cannot distinguish verified fact from unverified draft treats both as truth. Provenance labeling makes the distinction machine- and human-readable: AI-drafted, human-verified, source-linked, stale-since. Readers calibrate; retrieval systems filter; audits target.
This is the cheapest intervention on the record-to-people and record-to-model pathways, because it changes how contamination behaves without having to find it first: unverified material stops spreading at full credibility even before anyone cleans it.
The implementation detail that matters: labels must survive copying. A provenance stamp that vanishes when a paragraph is pasted into a new assessment governs nothing.
Addresses: Contaminated records read as fact · Model retrieving its own errors. Test a version of this lever in the PAN Lab.