Appropriate context-dependent artificial trust in human-machine teamwork

Abstract

As human-machine teams become a more common scenario, we need to ensure mutual trust between humans and machines. More important than having trust, we need all teammates to trust each other appropriately. This means that they should not overtrust or undertrust each other, avoiding risks and inefficiencies, respectively. We usually think of natural trust, that is, humans trusting machines, but we should also consider artificial trust, that is, artificial agents trusting humans. Appropriate artificial trust allows the agents to interpret human behavior and predict their behavior in a certain context. In this chapter, we explore how we can define this context in terms of task and team characteristics. We present a taxonomy that shows how trust is context-dependent. In fact, we propose that no trust model presented in the literature fits all contexts and argue that our taxonomy facilitates the choice of the trust model that better fits a certain context. The taxonomy helps to understand which internal characteristics of the teammate (krypta) are important to consider and how they will show in behavior cues (manifesta). This taxonomy can also be used to help human-machine teams’ researchers in the problem definition and process of experimental design as it allows a detailed characterization of the task and team configuration. Furthermore, we propose a formalization of the belief of trust as context-dependent trustworthiness, and show how beliefs of trust can be used to reach appropriate trust. Our work provides a starting point to implement mutual appropriate trust in human-machine teams.