interpretability

Chain-of-thought looks like the model showing its work, but it may not mirror the real computation. The signal is knowing CoT can be a post-hoc story, with evidence, and what that breaks for safety and oversight.

Most candidates answer with the 2015 ResNet answer and stop. The senior answer treats the residual stream as a shared communication bus, and that reframing is what makes logit attribution, the logit lens, and activation steering possible at all.

The textbook reflex ('drop one') is usually the wrong instinct, and whether collinearity matters at all depends on your model and what you want from it. This is the answer that tells rote apart from real understanding.

The built-in importance scores from XGBoost can rank a random ID above a vital feature, and stakeholders will base decisions on that bar chart. Here is why default importance misleads and what a careful answer reports instead.

Both explain one prediction by crediting features, but only one is backed by a uniqueness theorem. What interviewers reward is knowing SHAP's game-theory guarantees, LIME's instability, and when each fits. Here is the answer.

Raw gradient saliency maps are noisy and saturate. Integrated gradients cures both with two axioms and a path integral, yet the baseline choice quietly decides the answer. Here is what a careful candidate explains.

Regulators and enterprises increasingly require explanations for AI decisions, and the two terms get thrown around loosely. What lands is separating inherently interpretable models from post-hoc explanations and picking the right technique for the stakes.