Cognitive transparency and trust calibration in explainable AI systems: a psychological model of human-AI decision reliance

Abstract

Introduction Explainable artificial intelligence (XAI) is widely expected to improve transparency and support appropriate human reliance on AI-assisted decision-making. However, the psychological processes through which explainability relates to reliance remain insufficiently understood, and explainability, transparency, and trust have largely been examined separately. Objectives This study develops and tests a psychological model of cognitive transparency in which AI explainability, algorithmic transparency, and perceived AI accuracy are associated with human reliance on AI through cognitive understanding, cognitive trust, and trust calibration, with AI literacy as a boundary condition. Methods A cross-sectional online survey was completed by 342 AI users in South Korea in May and June 2026. Each participant evaluated one self-selected AI system that they had personally used; all constructs were measured by self-report. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4. Results AI explainability (β = 0.349) and algorithmic transparency (β = 0.297) were positively associated with cognitive understanding. Cognitive understanding (β = 0.388) and, more strongly, perceived AI accuracy (β = 0.492) were associated with cognitive trust. Cognitive trust was associated with trust calibration (β = 0.631), which was associated with human reliance on AI (β = 0.600). All hypothesized indirect effects were significant, and AI literacy strengthened the association between AI explainability and cognitive understanding (β = 0.192). The model explained 36%–54% of the variance in the endogenous constructs and showed medium predictive power. Conclusion The findings are consistent with a sequence in which explainable AI relates to reliance through users’ understanding and trust judgments. Because all constructs were self-reported and measured at a single point in time, the results describe associations among users’ perceptions rather than causal effects, independently verified calibration, or objectively appropriate reliance. The findings suggest that AI developers in domains such as healthcare, education, and finance, where calibrated reliance is critical, should pair user-facing explanations with information about system accuracy and with AI literacy support.