Modeling AI Overreliance as a Complex Adaptive System

New research models AI overreliance as a population-level process, challenging the common individual-user focus. The study indicates that environmental factors like task difficulty and AI quality are primary drivers of overreliance and calibration regret. While social learning fosters consensus among users, it does not inherently lead to increased overreliance, suggesting that collective wisdom can influence perceptions without exacerbating inappropriate trust in AI systems.

Why it matters

This research provides a foundational understanding of how AI reliance manifests at a population level, moving beyond individual user interactions. It highlights the critical interplay between AI system design, task environment, and social dynamics in shaping appropriate or inappropriate trust in AI, which is crucial for scalable AI deployment and risk management.

Key insights

• The effectiveness of AI assistance is more dependent on appropriate user reliance (trusting when correct, verifying when incorrect) than on the AI model's intrinsic accuracy.
• AI reliance is modeled as a population process where agents update Bayesian beliefs about AI quality through individual task solving, accepting AI answers, or verifying them.
• Networking enables agents to learn from peers, influencing collective perceptions of AI performance.
• Environmental conditions, specifically task difficulty and AI quality, are key determinants of the baseline levels of both AI overreliance and calibration regret within a population.
• Social learning mechanisms within a networked population contribute to consensus regarding AI quality.
• Contrary to potential assumptions, social learning does not increase overall AI overreliance; instead, it appears to preserve the mean level of reliance within the population.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.19616