Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs

Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources. This paper introduces Provenance Erasure Rate (PER) as a metric measuring the proportion of source-dependent claims in an AI-composed output that are presented without explicit attribution. PER does not ask whether an output is true; it asks whether the sources that made the output possible remain visible inside the composition. A motivating case study documents a Google AI Overview that constructed a false biography of a living author from real fragments in the author's published poetry: every fragment survived compression, but their provenance and meaning did not. PER for this output = 1.0 (total…