Presentation
Understanding Multi-Referent Trust in AI-Supported Evacuations: The Role of Transparency and Altruism
DescriptionTrust research in human-AI interaction (HAI) over the past decades has identified various factors influencing trust, aiming to explain how it forms, develops, and is violated, primarily within dyadic relationships between a single human and an AI agent. The current study addresses the gap of limited exploration in non-dyadic HAI scenarios by examining trust dynamics across two referents: trust in AI and trust in other humans. Using a custom-developed simulated mass evacuation testbed, we focus on a multi-operator-single-AI (MOSA) scenario, where multiple individuals need to evacuate to a safe area with the assistance of an AI guide. Participants can also choose to report roadblocks to the AI, aiding others at a personal cost. We investigate trust dynamics in both the AI and other humans, specifically examining how trust changes after passing each waypoint. Our goal is to understand the effects of information transparency (i.e., the degree of information shared about individuals’ reporting behaviors) and individual compliance and reporting behaviors (at time t) on trust dynamics (trust[t+1] − trust[t]). The results underscore the importance of holistically considering system transparency alongside behavioral and situational factors when analyzing trust dynamics. Furthermore, the study highlights that trust dynamics vary significantly depending on the referent.
Event Type
Creating AI that Works for People
Lecture
TimeThursday, October 16th12:10pm - 12:30pm CDT
LocationGrand B
Cognitive Engineering & Decision Making
Creating AI that Works for People: Human-Centered Innovation