Using agent features to influence user trust, decision making and task outcome during human-agent collaboration

Optimal performance of collaborative tasks requires consideration of the interactions between intelligent agents and their human counterparts. The functionality and success of these agents lie in their ability to maintain user trust; with too much or too little trust leading to over-reliance and under-utilisation, respectively. This problem highlights the need for an appropriate trust calibration methodology with an ability to vary user trust and decision making in-task. An online experiment was run to investigate whether stimulus difficulty and the implementation of agent features by a collaborative recommender system interact to influence user perception, trust and decision making. Agent features are changes to the Human-Agent interface and interaction style, and include presentation of a disclaimer message, a request for more information from the user and no additional feature. Signal detection theory is utilised to interpret decision making, with this applied to assess decision making on the task, as well as with the collaborative agent. The results demonstrate that decision change occurs more for hard stimuli, with participants choosing to change their initial decision across all features to follow the agent recommendation. Furthermore, agent features can be utilised to mediate user decision making and trust in-task, though the direction and extent of this influence is dependent on the implemented feature and difficulty of the task. The results emphasise the complexity of user trust in Human-Agent collaboration, highlighting the importance of considering task context in the wider perspective of trust calibration.