Trust fatigue is the pattern where people’s trust in AI assistance grows during a session while their verification effort declines. In Megagon Labs’ CAIS 2026 study, participants checked intermediate outputs carefully at first, then accepted plausible-looking results with lighter inspection as the session wore on. Systems can counter it by surfacing likely failure points instead of relying on manual checking of every node.
