AI fact-checking API: check claims against the live web with typed, cited answers

AI fact-checking typically involves detecting checkable claims, matching them to prior reviews, retrieving evidence, and assigning a verdict such as supported, contradicted, or not enough evidence. The comparison describes three complementary approaches: web-enabled LLMs, which achieved the highest accuracy on Parallel’s difficult PWS-Decisions benchmark but were slower and more expensive; Google’s Fact Check Tools API, which finds existing professional fact checks; and Parallel’s web-grounded Decisions API, which retrieves live web evidence and returns verdict probabilities and citations quickly at lower cost. In a small six-claim multilingual test conducted by Parallel, its API correctly handled five claims, while a changed model price was incorrectly supported but assigned low enough confidence to warrant human review. The recommended implementation uses one claim per request, an explicit insufficient-evidence option, confidence-based routing, stored citations and check dates, and human review for uncertain, refused, sensitive, or time-sensitive claims. The account notes that automated tools can scale verification and provide audit trails, but cannot replace human judgment about context, misleading framing, or final published corrections.

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