How would you improve AI trust and reliability?

### Signal to interviewer

I can improve trust by aligning technical reliability, UX transparency, and operational response into one measurable system.

### Clarify

I would define trust failure types: factual errors, inconsistency, unsafe output, and opaque behavior. I would segment by use case risk because trust expectations vary by task.

### Approach

Use a trust stack model: strengthen grounding and uncertainty, expose clear product controls, and institutionalize quality monitoring with incident response.

### Metrics & instrumentation

Primary metric: trusted-task completion without AI-caused rework. Secondary metrics: correction burden, repeat usage after errors, and confidence calibration quality. Guardrails: high-severity incidents, harmful output reports, and regression detection lag.

### Tradeoffs

More transparency improves confidence but can clutter UX. Conservative policies reduce harm but may lower perceived usefulness.

### Risks & mitigations

Risk: hidden quality drift; mitigate with live eval dashboards. Risk: user overtrust; mitigate with uncertainty messaging. Risk: slow recovery from incidents; mitigate with explicit rollback and ownership runbooks.

### Example

In a finance assistant, responses include cited policy snippets and confidence levels, with mandatory review for high-impact recommendations.

### 90-second version

Treat trust as a product system, not a messaging problem. Combine reliable behavior, transparent controls, and fast operational recovery, then measure trusted completion directly.