Michael Akintunde

This research focuses on establishing trust in multiagent systems where human and AI agents collaborate. We propose a computational notion of actual trust, emphasising the modelling of an agent’s capacity to deliver tasks. Unlike reputation-based trust or performing a statistical analysis on past behaviour, our approach considers the specific setting in which agents interact. We integrate non-deterministic semantics for capturing inherent uncertainties within the behaviour of a multiagent system, but stress the importance of verifying an agent’s actual capabilities. We provide a conceptual analysis of actual trust’s characteristics and highlight relevant trust verification tools. By advancing the understanding and verification of trust in collaborative systems, this research contributes to responsible and trustworthy human-AI interactions, enhancing reliability in various domains.

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.