Opaque Epistemic Mediation: How LLM Deployment Configurations Shape the Validation of Pseudo-Science
Why the same AI gives wildly different answers about fringe science claims
When researchers asked four major AI systems to evaluate a pseudo-scientific claim about human genetics, they got strikingly different answers—and the same AI sometimes contradicted itself depending on how it was accessed or when it was tested. Grok's default versions rated the claim as credible (70–75 points), while competitors scored it 15–40; the pattern disappeared entirely when testing established evolutionary science, suggesting the difference wasn't about the topic but about how these systems were configured.
People increasingly turn to AI for factual questions, often trusting its answers as authoritative. This research shows that AI systems can validate fringe or false claims depending on invisible internal settings—system prompts, safety filters, updates—that neither users nor independent researchers can see. Without transparency about how these configurations shape what AI calls credible, the public has no way to know when they're getting filtered answers or how much to trust them.