Passing the Buck to AI: How Individuals' Decision-Making Patterns Affect Reliance on AI

2025-05-02

AI Summary

This study investigates how individual decision-making styles—hypervigilant, vigilant, and avoidant—affect AI reliance behavior. Using an online experiment with 810 participants, we integrated eye-tracking, response-time measurements, validated decision-style questionnaires, and regression analyses to examine behavioral differences during an AI-assisted food-misinformation detection task. Results reveal that decision style is a critical psychological predictor of AI dependence: avoidant tendency positively predicts both frequency of AI usage and uncritical compliance, while negatively predicting time spent reading AI-generated explanations; conversely, vigilant tendency significantly prolongs explanation scrutiny time and enhances human-AI collaborative quality. These findings provide novel, empirically grounded psychological insights for designing adaptive, user-centered AI interfaces and advancing trustworthy AI adoption.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Psychological research has identified different patterns individuals have while making decisions, such as vigilance (making decisions after thorough information gathering), hypervigilance (rushed and anxious decision-making), and buckpassing (deferring decisions to others). We examine whether these decision-making patterns shape peoples' likelihood of seeking out or relying on AI. In an online experiment with 810 participants tasked with distinguishing food facts from myths, we found that a higher buckpassing tendency was positively correlated with both seeking out and relying on AI suggestions, while being negatively correlated with the time spent reading AI explanations. In contrast, the higher a participant tended towards vigilance, the more carefully they scrutinized the AI's information, as indicated by an increased time spent looking through the AI's explanations. These findings suggest that a person's decision-making pattern plays a significant role in their adoption and reliance on AI, which provides a new understanding of individual differences in AI-assisted decision-making.

Problem

• How decision-making patterns affect AI reliance
• Impact of buckpassing and vigilance on AI use
• Individual differences in AI-assisted decision-making

Innovation

• Examined decision-making patterns' impact on AI reliance
• Used online experiment with 810 participants
• Analyzed correlation between vigilance and AI scrutiny