an interpretation illusion

Humans are pattern-finding animals: we see faces in clouds and stories in random noise. The same instinct is dangerous when you peer inside a neural network. An interpretation illusion is a tidy, convincing explanation of what some part of a model is doing that fits the evidence you looked at — yet is not the true mechanism. The story feels right, the pictures look clean, and the model still works exactly the way you say it does, for the wrong reason.

These illusions arise easily because interpretability evidence is often correlational and cherry-picked. A neuron's top-activating examples might all be dogs, so you call it 'the dog neuron' — but on a broader dataset it turns out to fire just as much for fur textures and for the word 'loyal', and your label was an artifact of which examples you happened to inspect. A linear probe might decode a concept beautifully while the model never uses that information to decide anything. A feature visualization can be a compelling image that nonetheless misrepresents what actually drives the unit. In each case a plausible narrative outran the actual cause.

Interpretation illusions matter enormously for safety, because the whole value of interpretability is that we can trust what it tells us — and a confident false reading is worse than no reading at all, since it manufactures unjustified trust. It is also why the field leans hard on causal tests like activation patching and ablations rather than eyeballing: a feature visualization or a probe is evidence, not proof. The practical lesson is humility — treat every clean-looking interpretation as a hypothesis that must survive intervention before you believe it.