interpretability

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

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

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

Both explain a single prediction by attributing it to features, but only one rests on a uniqueness theorem. The signal is knowing SHAP's game-theory guarantees, LIME's instability, and when each is the right tool. Here is the answer.

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

Regulators and enterprises increasingly demand explanations for AI decisions, and the two terms get used loosely. The signal is separating inherently interpretable models from post-hoc explanations and naming the right technique for the stakes.