Determinants of system transparency and its influence on trust in and reliance on unmanned robotic systems

Abstract

Increasingly autonomous robotic systems are expected to play a vital role in aiding humans in complex and dangerous environments. It is unlikely, however, that such systems will be able to consistently operate with perfect reliability. Even less than 100% reliable systems can provide a significant benefit to humans, but this benefit will depend on a human operator's ability to understand a robot's behaviors and states. The notion of system transparency is examined as a vital aspect of robotic design, for maintaining humans' trust in and reliance on increasingly automated platforms. System transparency is described as the degree to which a system's action, or the intention of an action, is apparent to human operators and/or observers. While the physical designs of robotic systems have been demonstrated to greatly influence humans' impressions of robots, determinants of transparency between humans and robots are not solely robot-centric. Our approach considers transparency as emergent property of the human-robot system. In this paper, we present insights from our interdisciplinary efforts to improve the transparency of teams made up of humans and unmanned robots. These nearfuturistic teams are those in which robot agents will autonomously collaborate with humans to achieve task goals. This paper demonstrates how factors such as human-robot communication and human mental models regarding robots impact a human's ability to recognize the actions or states of an automated system. Furthermore, we will discuss the implications of system transparency on other critical HRI factors such as situation awareness, operator workload, and perceptions of trust.

The Effects of Agent Transparency on Human Interaction with an Autonomous Robotic Agent

“…We use Chen et al's (2014) model of agent transparency to examine the impact of agent transparency information on operators' trust in the autonomous robotic agent, SA of the agent's display, and workload while using the system. The use of SAT levels allows us to examine differing amounts of agent transparency on a systematic level, starting with trust.…”

Development of a Human Factors Roadmap for the Successful Implementation of Industrial Human-Robot Collaboration

“…robot), mental models are used to assist the user perceive and interpret the entity's intentions and actions [21]. At the same time, it must be noted that humans tend to have incomplete or even inaccurate mental models [22]. In an industrial HRC scenario humans will be requested to share the same workspace and collaborate with an industrial robot to complete a task.…”

“…Also, through exposure they will be in a position to identify factors that diminish or enhance the robot's ability to perform as well as detect cues that suggest a potential malfunction. According to [22] trust can be calibrated by providing an accurate understanding of the factors that may lead the robot to fail and the outcomes of those failures. To leverage this potential and enable effective HRC, it is proposed that operator empowerment can be a key strategy.…”

Assessing Technology in the Absence of Proof

“…Recently there has been a marked increase in attention for other, more subtle trust cues in human-40 system interaction than outcome feedback, such as goal similarity (Verberne, Ham, & Midden, 2012) 41 and cues conveying transparency and system rationale (e.g., De Visser et al 2014; Helldin et al, 42 2013;Ososky et al, 2014; Thill, Hemeren, & Nilsson, 2014). Nevertheless, the effects on trust of 43 direct experiences in the absence of outcome feedback have, to our knowledge, not received any 44 attention in human factors research.…”

“…Information obtained from process feedback does not necessarily have 36 anything to do with actual algorithms and functions employed by the system (such as cost functions 37 used by route planners to calculate routes) but is the result of the users' information processing based 38 on the cues provided to them via a system's interface displays. 39Recently there has been a marked increase in attention for other, more subtle trust cues in human-40 system interaction than outcome feedback, such as goal similarity (Verberne, Ham, & Midden, 2012) 41 and cues conveying transparency and system rationale (e.g., De Visser et al 2014; Helldin et al, 42 2013;Ososky et al, 2014; Thill, Hemeren, & Nilsson, 2014). Nevertheless, the effects on trust of 43 direct experiences in the absence of outcome feedback have, to our knowledge, not received any 44 attention in human factors research.…”