Measuring trust through user behavior

Trust is hard to measure directly, but user actions reveal it. Teams need clear ways to track if users trust AI the right amount. Good metrics change when products change, show meaningful patterns, and work in different situations. Watch for both extremes. Users accepting every AI suggestion might trust too much. Those always rejecting good predictions might trust too little.

Short-term metrics show quick reactions. Track new user responses after onboarding. See if explanations change behavior within days. Long-term patterns reveal more. Power users often reject more suggestions at first while learning limits. Then acceptance rises as they learn to use AI better. This U-shape shows healthy learning. Casual users might show flat rates, never building real confidence or doubt.

Different groups need different tracking. Doctor trust metrics differ from patient metrics for medical AI. Mix methods: A/B tests for features, surveys for feelings, analytics for actions. Stable trust levels can be good after big changes. They show users found their comfort zone. Just check it's healthy stability, not worrying stagnation. Trust measurement should grow smarter as you learn your users better.