The short answer
Users forgive a feature that is sometimes unsure. They do not forgive one that is confidently wrong, because after that they check everything and the time saving disappears.
Design for visible sourcing, honest uncertainty and easy correction.
Patterns that build trust
| Pattern | Effect |
|---|---|
| Cite the source | User can verify in seconds |
| Say when unsure | Sets expectation before the error |
| Show the input used | Explains an odd answer |
| One-click correction | Turns a failure into a contribution |
| Preview before acting | Stops mistakes becoming consequences |
Previewing before acting is the most important one for anything that changes data or sends a message. It converts a class of serious errors into a class of ignorable ones.
Handle uncertainty explicitly
- Decide what low confidence means for this feature.
- Present low confidence results differently, not just with a number.
- Offer the user an alternative path when confidence is low.
- Decline rather than guess where a wrong answer is costly.
- Track how often you decline, because too often is also a failure.
Declining is underused. For some tasks, saying the system cannot answer is far better product behaviour than producing something plausible.
Make correction worth the user's time
If correcting takes longer than doing it manually, people stop correcting and start ignoring. Corrections should be one action and should visibly improve the next result where possible.
Where a correction does feed back into behaviour, say so. Users invest more effort when they can see it matters.
Do not hide that it is AI
- Label AI-generated output clearly
- Be honest about limitations in the interface, not only in documentation
- Never imply a human reviewed something that was not reviewed
- Make it clear what is sent to a third party, if anything is
- Give an opt-out where the feature is not essential
Concealment is the fastest route to losing trust permanently, and it is increasingly a regulatory issue as well as a product one.