Why do we Trust Chatbots? From Normative Principles to Behavioral Drivers
This addresses the issue of trust calibration in conversational AI for users, but it is incremental as it builds on existing discussions without introducing new empirical data or methods.
The paper tackles the problem of trust in chatbots by examining how user trust is often shaped by behavioral mechanisms and design choices rather than normative principles, and proposes reframing chatbots as skilled salespeople to highlight this distinction.
As chatbots increasingly blur the boundary between automated systems and human conversation, the foundations of trust in these systems warrant closer examination. While regulatory and policy frameworks tend to define trust in normative terms, the trust users place in chatbots often emerges from behavioral mechanisms. In many cases, this trust is not earned through demonstrated trustworthiness but is instead shaped by interactional design choices that leverage cognitive biases to influence user behavior. Based on this observation, we propose reframing chatbots not as companions or assistants, but as highly skilled salespeople whose objectives are determined by the deploying organization. We argue that the coexistence of competing notions of "trust" under a shared term obscures important distinctions between psychological trust formation and normative trustworthiness. Addressing this gap requires further research and stronger support mechanisms to help users appropriately calibrate trust in conversational AI systems.