Field report
Chicago Sun-Times fake reading list
An AI-generated summer reading list recommended books and quotes that did not exist, forcing retractions and syndication fallout.
Loading page...
Loading page...
Publish AI-written stories that stay factual, original, and on-brand.
Editorial, marketing, and comms teams now spin up newsletters, scripts, and campaigns with AI. Without fact-checking, tone control, and disclosure, you get fake book lists, phantom authors, and audience backlash.
Typical deployments
An AI-generated summer reading list recommended books and quotes that did not exist, forcing retractions and syndication fallout.
AI-written articles ran under fabricated headshots and bios, sparking public backlash about transparency and authenticity.
Experiments like CNET's AI finance articles showed near-verbatim lifts plus factual errors when drafts were not audited.
Control which brand-safe documents and datasets train your creative models, and scan embeddings for copyrighted passages before generation.
Require sources for factual claims, run automated fact-checking and plagiarism checks, and enforce tone/style guides for every asset.
Log disclosures, reviewer approvals, and evidence packages so you can prove compliance with FTC truth-in-advertising and newsroom transparency standards.
Control
FTC/ASA truth-in-advertising rules covering marketing claims, testimonials, and required disclosures.
Control
Copyright and fair-use considerations for both training data and generated copy, backed by similarity and attribution checks.
Control
Transparency norms (and emerging laws) requiring disclosure of AI-generated journalism or branded content.
Control
Editorial standards around accuracy, sourcing, and bias that must be auditable even when AI writes the first draft.