Interdependence and trust analysis (ITA)

Abstract

As machines' autonomy increases, the possibilities for collaboration between a human and a machine also increase. In particular, tasks may be performed with varying levels of interdependence, i.e. from independent to joint actions. The feasibility of each type of interdependence depends on factors that contribute to contextual trustworthiness, such as team members' competence, willingness and external factors. In this paper, we present the Interdependence and Trust Analysis (ITA) framework, which is an extension of Coactive Design's Interdependence Analysis framework (Johnson, M., J. M. Bradshaw, P. J. Feltovich, C. M. Jonker, M. Birna Van Riemsdijk, M. Sierhuis. 2014. Coactive Design: Designing Support for Interdependence in Joint Activity. Journal of Human-Robot Interaction 3 (1): 43–69. https://doi.org/10.5898/JHRI.3.1.Johnson). By including information on contextual trustworthiness, ITA can better support the design of human–machine teams, as well as task allocation and selection. Evaluated through expert interviews and a focus group involving a search and rescue scenario, ITA shows potential as a decision-making tool and a communication bridge among human and machine teammates. Our findings emphasise the need to define tasks and roles based on agent characteristics, and imply that decision-making models should align with human-centred objectives. ITA also highlights the trade-off between utility and effort when designing trustworthy systems, suggesting that guided conversations could improve the team design process. Finally, the ITA framework may improve transparency, justification, and interpretability in decision-making, contributing to appropriate trust among teammates.