Advancing Human-Robot Teams: A Framework for Understanding Swift Trust

Abstract

In this paper, we describe how swift trust in human-robot (HR) teams is developed through the incorporation of surface-level cues and imported information. Surface-level cues are physical characteristics whereas imported information serves to shape preconceived notions about the robot itself. A multidimensional continuum is used to explore the effects of varying surface-level cues and imported information on the type of swift trust that may form as a result: high, medium, and low swift trust. Similar surface cues and positive information suggest higher swift trust development, whereas, negative imported information and differing surface cues evoke low swift trust. Surface cues and imported information that are incongruous leads to medium swift trust formation. This paper offers insights into the development of swift trust depending on the specific traits relevant to both the human and robot team members.