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A customer wants to auto-decide on a high-stakes classifier. How do you make the probabilities safe to act on?

When a model auto-approves loans or routes patients, the probability is the product, not a dashboard number. The calibration-plus-abstention design that lets a customer trust an automated decision, and the subgroup trap that gets it pulled in audit.

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

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FEDITOR'S NOTE

The screen is whether you separate three things candidates blur together: discrimination (ranking), calibration (the number means what it says), and the decision policy (threshold plus abstain band). The reserved follow-up is subgroup calibration, a model can be calibrated overall and badly miscalibrated for a protected segment, which is exactly what a regulator or a postmortem surfaces; naming reliability-by-subgroup and an abstention lane before being asked is the staff signal.

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