robustness

Most real datasets carry wrong labels, and they quietly cap your accuracy. Hand-cleaning all of it does not scale. Here is how to surface the bad labels and train robustly around them.

Your eval set is clean, well-punctuated, and phrased the way you think about the problem. Your users are not. The move that turns this from a vibe check into an engineering artifact is measuring invariance rather than accuracy, and gating on the number it produces.

A classic ML-security question that trips up people who only know clean-data accuracy. What lands is grasping why models are brittle, the realistic threat model, and the fact that no single defense holds. Here is the honest answer.