arXiv subjects

Johan Sebastián Galindez-Acosta

Publications and source records attributed to Johan Sebastián Galindez-Acosta.

2 recordsLinked to original sources

arXiv2025

A race to belief: How Evidence Accumulation shapes trust in AI and Human informants

The integration of artificial intelligence into everyday decision-making has reshaped patterns of selective trust, yet the cognitive mechanisms behind context-dependent preferences for AI versus human informants remain unclear. We applied a Bayesian Hierarchical Sequential Sampling Model (HSSM) to analyze how 102 Colombian university students made trust decisions across 30 epistemic (factual) and social (interpersonal) scenarios. Results show that context-dependent trust is primarily driven by differences in drift rate (v), the rate of evidence accumulation, rather than initial bias (z) or response caution (a). Epistemic scenarios produced strong negative drift rates (mean v = -1.26), indicating rapid evidence accumulation favoring AI, whereas social scenarios yielded positive drift rates (mean v = 0.70) favoring humans. Starting points were near neutral (z = 0.52), indicating minimal prior bias. Drift rate showed a strong within-subject association with signed confidence (Fisher-z-averaged r = 0.736; 95 percent bootstrap CI 0.699 to 0.766; 97.8 percent of individual correlations positive, N = 93), suggesting that model-derived evidence accumulation closely mirrors participants' moment-to-moment confidence. These dynamics may help explain the fragility of AI trust: in epistemic domains, rapid but low-vigilance evidence processing may promote uncalibrated reliance on AI that collapses quickly after errors. Interpreted through epistemic vigilance theory, the results indicate that domain-specific vigilance mechanisms modulate evidence accumulation. The findings inform AI governance by highlighting the need for transparency features that sustain vigilance without sacrificing efficiency, offering a mechanistic account of selective trust in human-AI collaboration.

cs.HC↗

arXiv2025

Trust in AI emerges from distrust in humans: A machine learning study on decision-making guidance

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.

cs.HC↗