Trust Should Match Capability
An AI feature can appear confident even when its evidence is incomplete. Polished language, immediate responses, and human-like conversation encourage users to assume more reliability than the system has earned. The UX problem is not simply building trust; it is helping users calibrate trust to the situation.
The interface should communicate what the system used, what it produced, and what remains uncertain. The level of explanation should increase with the consequence of the action.
Keep Judgment with the User
Trustworthy AI interactions provide meaningful control:
- Show the basis: Cite source material or display the inputs that shaped a recommendation when they can be inspected.
- Signal uncertainty specifically: Explain missing data or competing interpretations instead of showing an unexplained confidence score.
- Preview consequential actions: Let users review recipients, changes, or transactions before the system executes them.
- Support correction: Make it easy to edit output, provide feedback, and recover from an incorrect action.
Good AI UX does not ask users to accept or reject the system wholesale. It gives them enough context to decide when to rely on it and when to intervene.