How to Evaluate AI Claims
Every week brings bold new AI claims. Learn to separate signal from noise with a structured evaluation framework, then test your skills spotting common red flags.
Claim Evaluation Scorer
Select a claim below, then score it across six criteria. After scoring all claims, see how your assessments compare with expert analysis.
Red Flag Detector
Learn the 10 most common red flags in AI claims, then test yourself: can you spot which flags are present in real-world examples?
Key Takeaways
Every AI claim should be evaluated against evidence quality, specificity, reproducibility, source credibility, conflict of interest, and peer review. Most impressive-sounding claims collapse when tested against even two or three of these criteria. A claim that scores low on specificity and lacks peer review is almost always marketing, not science.
Unreliable AI claims rarely exhibit just one red flag. Vague metrics often appear alongside cherry-picked benchmarks and missing baselines. When you spot one red flag, actively look for others — the presence of multiple flags is a much stronger signal than any single one.
The most powerful skill in AI literacy is not scepticism — it is knowing the right follow-up question. Instead of dismissing a claim outright, ask: "Compared to what?", "Under what conditions?", "Says who?", and "What happens when it fails?" These questions separate genuine advances from inflated promises without requiring deep technical knowledge.
When the entity making a claim also profits from it being believed, the bar for evidence should be significantly higher. This does not mean the claim is false — many legitimate advances are announced by interested parties. But it means independent verification becomes essential before accepting the claim at face value.