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Your training data was collected with selection bias. How do you detect it and correct for it?

If labels only exist for the cases you already acted on, the model learns a distorted world: great offline, blind to everyone you never saw. Worse, its own decisions pick the next labels. Here is how to spot it and counter it.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

If labels only exist for the cases you already acted on, the model learns a distorted world: great offline, blind to everyone you never saw. Worse, its own decisions pick the next labels. Here is how to spot it and counter it.

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