March 2026
Designing for User Trust in AI Products
A practical framework for building AI features users trust: transparent inputs, controllable outputs, and clear decision boundaries.
Why Trust Breaks
Most AI products fail adoption not because models are weak, but because users cannot understand or control how decisions are made.
What Works
Trust improves when users can inspect assumptions and adjust parameters.
- Show what inputs matter
- Provide what-if controls
- Position output as support, not replacement