Well, consider strategic point of view. Suppose that a system (humans) is known for it's poor performance at evaluating the claims without performing direct experimentation. Long, long history of such failures.

Consider also that a false high-impact claim can ruin ability of this system to perform it's survival function, with again a long history of such events; the damage is proportionally to the claimed impact. (Mayans are a good example, killing people so that the sun will rise tomorrow; great utilitarian rationalists they were; believing that their reasoning is perfect enough to warrant such action. Note that donating to a wrong charity instead of a right one kills people)

When we anticipate that a huge percentage of the claims will be false, we can build the system to require evidence that if the claim was false the system would be in a small probability world (i.e. require that for a claim evidence was collected so that p(evidence | ~claim)/p(evidence | claim) is low), to make the system, once deployed, fall off the cliffs less often. The required strength of the evidence is then increasing with impact of the claim.

It is not an ideal strategy, but it is the one that works given the limitations. There are other strategies and it is not straightforward to improve performance (and easy to degrade performance by making idealized implicit assumptions).