AI & Technical question

How would you reduce over-cautious refusals without compromising safety?

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 AI product judgment on the precision and recall tradeoff between helpfulness and safety in model behavior.

Define over cautious refusal precisely: cases where the model declines a request that is actually benign, distinct from correct refusals.
Separate the problem into measurement and fix: first quantify how often this happens and on what request types, then address the cause.
Propose a measurement approach: sample real refused queries, have humans label which were truly unsafe versus over cautious, and track that rate over time.
Identify likely causes: overly broad classifier categories, training data that conflated adjacent risky and safe topics, or prompts that pattern match unsafe language.
Propose fixes: sharpen the classifier with more granular categories, add context aware exceptions, for example medical or security research framed with legitimate intent.
Confirm with the interviewer whether this is about the safety classifier, the model's own judgment, or both, since the fix differs.

Explicitly separates false positive refusals from true positive refusals so the fix targets the right failure mode instead of loosening safety broadly.
Proposes a concrete metric, an over refusal rate on a held out benign query set, tracked release over release.
Suggests contextual signals, like account history or stated professional use, as a lever rather than only rewriting static rules.
Notes the tradeoff explicitly: reducing over refusals must not raise the true unsafe completion rate, and proposes tracking both together.

Treating this as purely a prompt engineering fix rather than a data and classifier problem.
Proposing to loosen safety broadly without a way to detect if unsafe completions increase as a result.
No concrete metric for over caution, making progress unmeasurable.

Chapters of the AI PM course that teach what this question tests.