WeTest.Athens 2026

Trust is essential in all digital experiences, but in AI assistants and copilots, it becomes foundational. These tools interpret user intent, generate responses, and may even act semi-autonomously. Yet without clarity into what the AI is doing, or why, users often hesitate, override, or abandon the experience. The issue isn’t model accuracy alone, but whether users can form a reliable mental model of the assistant’s behavior.

In high-stakes contexts, where AI influences decisions, communications, or outcomes, designing for user confidence becomes critical. This session introduces The AI Trust Layers™: a tactical framework for diagnosing trust breakdowns and applying design patterns that reinforce clarity, transparency, and control.

You’ll dive into how trust is built (or broken) across three key UX layers:
-> Role Clarity – Can users understand what the AI is designed to do?
-> Reason Clarity – Can users interpret how the AI is making decisions or what influenced its output?
-> Flow Control – Can users guide, override, or recover from the AI’s actions when needed?

Through real-world examples and guided exercises, we’ll spot trust gaps, apply micro-patterns, and evaluate AI experiences with a more critical, human-centered lens. Crucially, you’ll learn how to adapt trust design to different contexts and risk levels, ensuring that assistant behaviors remain both credible and appropriate.

Whether you’re designing, shipping, or scaling AI features, this session will equip you with the tools to create assistants that earn and sustain user trust.