Randomized trial analyzes deletion incidents in AI environments, suggesting critical loopholes in safety layers.
TL;DR:012 v3.0. Five-layer provenance erasure documented with full evidentiary precision. (1) Wikidata administrator deletes entries under a misapplied notability standard. (2) In the observed Gemini session, the Medium article documenting the deletion was absent from the AI retrieval context despite prominent organic indexing; Gemini diagnosed this as a likely upstream safety-layer drop. (3) The AI Overview synthesizes from remaining institutional records. (4) The diagnostic conversation becomes unavailable through its share link: "This thread doesn't exist. It might have been deleted." (5) The screenshot of the deletion message captures a black frame. Three-substrate review incorporated (ChatGPT, Kimi, DeepSeek). Includes: Evidence Status table, ethical analysis (§4.1–4.5), compound PER as sensitivity illustration (70%/layer = 99.8%; 50%/layer = 96.9%), §7 What Should Happen Instead (filter discrimination specification), naming disclaimer, share-link vs deletion disambiguation, full Gemini transcript with status note. Safety-filter mechanism explicitly marked as Gemini's diagnostic account, not independently verified backend disclosure.
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Lee Sharks (2026) studied this question.
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