Rebuilding Public Trust Through Algorithmic Transparency
BYT News bridges traditional editorial rigor with cutting-edge artificial intelligence to combat fake news, verify source reputation, and log publishing transparency across platforms.
The AI Credibility Verification Engine
How our platform aggregates news feeds and audits stories before publication
Stories Arrive Already Edited
Our newsroom system handles sourcing and drafting upstream, then hands finished stories to this site. Every story keeps the name of the newsroom it originated with and a link back to the original report.
Claims Are Separated From Framing
When you run a story through our scanner, a language model pulls out the specific factual claims it makes — who did what, when, and where — and sets aside the adjectives.
Each Claim Is Checked Against Live Coverage
We search roughly 21 established newsrooms in parallel for reporting on the same event, then ask whether they support the claim, contradict it, or have not covered it at all.
A Score You Can Take Apart
Four signals combine arithmetically, not by model opinion: agreement with the coverage we found, plausibility, the reputation of the outlet behind it, and tone. If we could not confirm who published something, we say so and withhold the Credible badge rather than guessing.
Editorial & Research Leadership
Who is accountable for what appears here
Dr. Sarah Lin
Former Stanford AI Lab researcher specializing in NLP claim extraction and misinformation detection.
David Vance
20+ years of investigative journalism experience across international newsrooms and wire services.
Amina Al-Hassan
Expert in media provenance, digital signature authentication, and synthetic content detection.
Our Open Credibility Pledge
Every score our scanner produces is advisory, shows the individual claims it checked, and names the outlets it found. Where we could not verify something, we tell you that instead of rounding it up to a verdict.