The Transparency Paradox: Decoupling Perceived and Behavioral Trust Through Trust Calibration in Human–AI Interaction
Luoman Ouyang, JungJoo Jahng, Suhan Woo · International Journal of Human-Computer Interaction · 2026
While explainable AI (XAI) aims to foster trust, its impact on behavioral reliance remains contested. Contrary to the common assumption that transparency linearly promotes reliance, our findings reveal a significant perception-behavior gap. Through a multi-round experiment, we demonstrate that while XAI improves perceived interpretability, it fails to facilitate immediate perceived trust recovery following a system error; instead causing a pronounced decline in behavioral reliance. These results suggest that transparency may function as a cognitive forcing function, triggering analytical System 2 processing that heightens user sensitivity to AI fallibility. This shift reflects a dynamic recalibration of reliance patterns, supported by improved decision accuracy in the XAI condition. Additionally, AI literacy moderates this process as a cognitive scaffold for lower-literacy users. Ultimately, transparency alone is insufficient for trust recovery, highlighting the need to design for trust calibration rather than trust maximization to ensure resilient human-AI collaboration.