AI trust design
AI trust design is how interfaces help users calibrate confidence through provenance, uncertainty, human escalation, cost transparency, and honest failure states. Trust belongs in the spec, not a polish pass before launch.
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Patterns to ship first
Frameworks
Production examples
Glossary
Frequently asked questions
What is AI trust design?
AI trust design is the set of UX patterns that help users decide when to rely on, verify, or reject AI outputs through citations, confidence signals, checkpoints, and honest limits.
Which trust patterns should I ship first?
Start with provenance (citations or source browser), explicit capability limits, and human checkpoints for irreversible actions. Add confidence scoring where wrong answers are costly.
Is trust design only for search products?
No. Agents, copilots, and creative tools need trust UX too: tool transparency, action receipts, and failure disclosure apply whenever models act or advise.
How does trust design relate to safety?
Trust UX makes safety visible: users see uncertainty, can escalate, and understand blast radius before approving agent actions.
Where can I benchmark trust UX?
Use teardown comparisons for citations, human-in-the-loop, and personalization across ChatGPT, Claude, Perplexity, and Gemini.