How mixed-quality data collapses authority signals and forces AI to infer what should be explicit “Why is AI saying the county issued this emergency alert when it clearly came from the city?” The response appears confident, naming an agency, quoting language, and presenting it as official guidance. But the attribution is wrong. The alert originated from a municipal department, not the county. The wording has been slightly altered, and the timestamp reflects an earlier version of the message. The answer reads as authoritative, yet it blends sources, compresses updates, and assigns responsibility incorrectly. The result is not just imprecise—it is misleading in a way that affects public understanding. How AI Systems Separate Content from Source AI systems do not interpret information as complete, intact documents. They process content by breaking it into fragments—sentences, phrases, and semantic units—then reconstruct meaning based on patterns across a broad dataset.…