Exploring the Dynamic Nature of Trust Using Interventions in a Human-AI Collaborative Task

. People are increasingly interacting with machines embedded with intelligent decision aids, sometimes in high-stakes environments. When a human user comes into contact with a decision-making agent for the first time, it is likely that the agent’s behaviour or decisions do not precisely align with the human user’s goals. This phenomenon, known as goal alignment , has been recognised as a critical concern for human-machine teams. Prior work has focused on the effect of automation’s behavioural properties, such as predictability and reliability, on trust in human-machine interaction scenarios. However, little is known about situations where automation’s capabilities are misaligned with humans’ expectations and its impact on trust. Even less is known about the effect of environmental factors on trust. We study the relationship between intervention behaviours and trust in a simulated navigation task where the human user collaborates with an agent with misaligned goals. We evaluate trust quantitatively using intervention frequency as a behavioural measure and qualitatively using self-reports. By advancing the understanding and measurement of trust in collaborative settings, this research contributes to the development of trustworthy and symbiotic human-AI systems.