Metrics question

What metrics would you define to measure both the effectiveness and blind spots of a rare-harms safeguards system, and how would you use those metrics to make shipping and iteration tradeoffs over time?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Tests defining both effectiveness and blind-spot metrics for a rare-event safety system and using them to drive shipping decisions over time.

Define effectiveness metrics: recall on known harm patterns via audit sampling, and time-to-detection for confirmed cases.
Define blind-spot metrics, such as a periodic audit of un-flagged traffic to estimate how much harm the system is missing.
Track false-positive rate on legitimate users as a guardrail alongside effectiveness.
Use trend over multiple periods, not a single snapshot, since a rare-event system needs time to distinguish signal from noise.
Tie shipping decisions to thresholds on these metrics, such as expanding automation only once recall and false positives both clear a bar.

Distinguishes metrics measurable directly, like flags, from ones needing sampling to estimate, like blind spots, and explains how to estimate the latter.
Uses audit sampling of un-flagged traffic as the core blind-spot metric, since you can't know what you didn't catch otherwise.
Proposes waiting for multiple measurement periods before acting on a rare-event metric, given natural volatility.
Ties a concrete iteration decision to the metrics, such as holding automation back until false positives drop below a set bar.

Only tracking metrics on flagged cases, which can't reveal what the system is missing.
Reacting to single-period noise in a low-volume metric as if it were a real trend.

How would you estimate blind spots without infinite audit capacity?
How many measurement periods would you want before trusting a change in this metric?