Article · Design
Designing for Trust in AI Products
Trust isn't a feature you ship — it's a hundred small decisions. Here's a framework for getting them right.
Trust is earned in the details
Users don't read your privacy policy — they feel your product. Every loading state, every confident-but-wrong answer, every undo that isn't there: these are the moments where trust is won or lost.
A working framework
We propose four levers: transparency (show your work), reversibility (make mistakes cheap), calibration (be confident only when you should be), and consent (no surprises). Together they turn an opaque model into a product people rely on.
None of this is about adding a 'trust' feature. It's about treating every interaction as a small promise — and keeping it.