Issue 01. Trust in AI

Yuliya Martinavichene 10/27/26 Yuliya Martinavichene 10/27/26

Beyond accuracy and transparency: the real product factors driving human-AI trust

UX wants transparency features, and engineers want 99% accuracy - but neither guarantees safe AI adoption. Real product trust relies on how predictable your errors are, whether users can easily override flawed outputs, and how well the system signals its own limits. Learn how to design AI tools that encourage calibrated reliance without creating safety hazards.

Yuliya Martinavichene 10/15/26 Yuliya Martinavichene 10/15/26

Trust in AI: Who’s doing the trusting?

Trust in AI is not a binary product metric; it is a dynamic cognitive attitude. While engineers track model accuracy, user reliance is governed by psychological, cultural, and organizational factors. To build safe and sticky AI products, product leaders must shift from maximizing raw usage to designing for calibrated reliance - mitigating both misuse (overtrust) and disuse (distrust).