Explain integrated gradients for attribution. Why use it over raw gradients, and how do you pick the baseline?
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.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
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.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
