Machine Learning System Design Interview, issue 41, May 29, 2026

The Average Feature Trap

Principal AI Engineer interview at Stripe, and the interviewer asks:

The business team refuses to deploy your new fraud detection neural network because they can’t explain to regulators why a specific high-value user was blocked. If a candidate hands them a global feature importance plot, why does that completely miss the mark, and how do you fix it for an audit?

Don’t say: I’ll pull up the global feature importance chart from our training run. It proves that transaction volume and IP velocity are the top risk factors across our entire dataset, which justifies the model’s behavior.

The dangerous fallacy of using global metrics to justify individual edge cases, and how elite engineers build real-time local explanation pipelines to save high-value user experiences.

The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.

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