🤖 AI Summary
This study investigates the formation mechanism of human trust in AI agents—particularly large language models—proposing the “transferrable trust” theory: when users distrust human agents, they strategically shift reliance toward AI perceived as more neutral, reliable, or competent, resulting in compensatory dependence. Method: Integrating sociodemographic and prior-trust variables, the study employs K-Modes/K-Means clustering to identify behavioral patterns and builds an interpretable XGBoost-SHAP model to predict AI adoption propensity. Contribution/Results: Low trust in humans, limited technology experience, and high socioeconomic status emerge as key predictors of strong AI trust. AI adoption reaches 28.29% in factual decision-making, whereas humans retain superiority in social/moral contexts. The model achieves a mean accuracy of 0.863. This work transcends conventional technology acceptance frameworks by systematically uncovering how social relational dynamics and cognitive distrust drive trust migration toward AI.
📝 Abstract
This study explores the dynamics of trust in artificial intelligence (AI) agents, 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 social psychology and technology acceptance models, the research addresses gaps in user-centric factors influencing AI trust. 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 trust were distinguished by human distrust, lower technology use, and higher socioeconomic status. Models demonstrated consistent performance (e.g., average precision up to 0.863).
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.