How do you balance safety vs performance in AI products?

### Signal to interviewer

I can align safety and performance with risk-adjusted governance rather than one-size-fits-all controls.

### Clarify

I would clarify harm classes, regulatory expectations, and acceptable failure boundaries by product flow.

### Approach

Use risk-tier performance envelopes: define immutable safety floors, then optimize latency and quality within each tier.

### Metrics & instrumentation

Primary metric: successful outcomes in safety-compliant sessions. Secondary metrics: refusal calibration quality, response usefulness, and escalation rate. Guardrails: high-severity safety incidents and trust complaints.

### Tradeoffs

More safety controls lower risk but can suppress utility. More performance focus raises utility but can increase unsafe behavior probability.

### Risks & mitigations

Risk: over-blocking useful outputs; mitigate with calibration reviews. Risk: under-detected harmful edge cases; mitigate with red-team coverage. Risk: inconsistent policy execution; mitigate with centralized policy engine.

### Example

Medical guidance flows use strict verification and handoff prompts, while writing suggestions use lighter moderation and faster generation.

### 90-second version

Set safety floors first, then optimize performance by risk tier. This approach protects trust while keeping user experience competitive where risk is low.