Trust in AI is rarely about perfect accuracy. It is about predictable behaviour, clear constraints, and respectful transparency when the system is uncertain. Users lose confidence when AI feels like a black box.
Make expectations explicit
Start with simple, user-friendly notices:
- What the assistant can and cannot do.
- When it may be wrong or incomplete.
- How data is handled in broad terms (see data minimisation).
Show evidence, not just confidence
Citations and grounding are one of the strongest trust levers. When the system uses retrieved sources, show them and encourage verification for high-impact decisions (see citations and grounding).
Give users control
Good AI UX includes control mechanisms:
- Edit and refine. Let users correct context and constrain the response (see UX patterns).
- Escalate to humans. Provide a clear fallback when stakes are high or uncertainty is detected (see human-in-the-loop).
- Clear modes. If the system is throttled or in a safer mode, say so.
Operationalise feedback loops
Feedback is not a "nice to have". It is operational telemetry. Capture user satisfaction, reasons for escalation, and common failure modes, then feed that into evaluation and improvement cycles (see usage analytics and evaluation loops).
Communicate changes
When behaviour changes, users should not be surprised. Use release notes for major model or policy changes so expectations stay aligned (see AI release notes).
Transparency is a governance control that improves adoption. When users understand the limits, they use AI more safely and more effectively.