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The dynamics of trust behavior in artificial intelligence (AI) agents are explored, particularly large language models (LLMs), by introducing the concept of ”deferred trust”, a cognitive mechanism where distrust in human agents redirects reliance toward AI perceived as more neutral or competent. Drawing on frameworks from cognitive psychology and technology acceptance models, the research addresses gaps in user-centric factors influencing AI reliance. Fifty-five undergraduate students participated in an experiment involving 30 decision-making scenarios (factual, emotional, moral), selecting from AI agents (e.g., ChatGPT), voice assistants, peers, adults, or priests as guides. Data were analyzed using K-Modes and K-Means clustering for patterns, and XGBoost models with SHAP interpretations to predict AI selection based on sociodemographic and prior trust variables. Results showed adults (35.05%) and AI (28.29%) as the most selected agents overall. Clustering revealed context-specific preferences: AI dominated factual scenarios, while humans prevailed in social/moral ones. Lower prior trust in human agents (priests, peers, adults) consistently predicted higher AI selection, supporting deferred trust as a compensatory transfer. Participant profiles with higher AI reliance were distinguished by human distrust, lower technology use, and higher socioeconomic status. Models demonstrated consistent performance (e.g., average precision up to 0.881). Findings challenge traditional models like TAM/UTAUT, emphasizing relational and epistemic dimensions in AI trust. They highlight risks of over-reliance due to fluency effects and underscore the need for transparency to calibrate vigilance. Limitations include sample homogeneity and static scenarios; future work should incorporate diverse populations and multimodal data to refine deferred trust across contexts.
Galindez-Acosta et al. (Sat,) studied this question.