ai-hallucination-fact-check-protocol

Fact-checking AI hallucinations requires an adapted SIFT protocol that classifies LLM error types and performs source reconstruction to verify whether cited sources exist and match the AI's statements.

The best way to teach digital literacy for AI fact-checking is through a structured classroom activity like a Hallucination Hunt, guided by a teacher think-aloud model. This demonstrates successful verification and how to discover fabricated citations.

Yes, you can use an AI-adapted SIFT method to detect fabricated statistics in LLM outputs by applying a hallucination taxonomy. This taxonomy classifies statistical invention and false consensus, matching verification steps to the specific claim type.

During citation verification, types of AI hallucinations classified include fabricated citations, statistical invention, misattribution, and false consensus. Categorizing these LLM error types ensures that verification steps match the specific claim being checked.

Lateral reading works for verifying false consensus claims when integrated into an AI-adapted SIFT workflow. It uses step-by-step verification moves to cross-check the AI's consensus statements against external sources during classroom practice scenarios.