AI-generated alt text for news images is becoming a standard accessibility and SEO practice — but AI systems are prone to confident misidentification of individuals, incorrect event labeling, and factual errors in scene descriptions. When these errors go unverified into published alt text, they create accessibility failures (screen-reader users receive incorrect descriptions), potential legal exposure (false identification of named individuals), and SEO damage (AI-generated alt text errors undermine image search ranking signals).
Omniscient AI verification of factual claims in AI-generated alt text takes seconds. A photo editor checking AI-generated text that says "Senator [Name] speaks at [Event] on [Date]" can run the specific factual claims through a three-engine check to confirm the individual identification, event attribution, and date accuracy before the alt text goes live. Engines that disagree on the identification flag the claim for manual verification through primary sources.
The quality signal extends beyond individual images. Publications whose AI-generated alt texts are consistently verified build a track record of image metadata accuracy that supports both accessibility compliance and image search authority — two benefits that compound over the publication's archive.