Trust Dynamic

Trust dynamics research investigates how trust in artificial intelligence (AI) systems, particularly robots and autonomous vehicles, evolves over time and across different contexts, aiming to understand and predict human reliance on these technologies. Current research focuses on modeling trust using dynamic structural equation modeling, graph neural networks, and agent-based simulations, often incorporating factors like human-robot interaction, system performance, and individual personality traits to predict trust levels and identify trust profiles (e.g., believers, oscillators, disbelievers). This work is crucial for improving the safety and usability of AI systems by enabling the development of trust-aware algorithms and personalized interfaces that calibrate user trust appropriately, thereby mitigating risks associated with over- or under-reliance.