Your training data was collected with selection bias. How do you detect it and correct for it?
When labels exist only for the cases you already acted on, the model learns a distorted world: strong offline, blind to everyone you never saw. Worse, its own decisions choose the next labels. Here is how to spot it and counter it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
When labels exist only for the cases you already acted on, the model learns a distorted world: strong offline, blind to everyone you never saw. Worse, its own decisions choose the next labels. Here is how to spot it and counter it.
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.
