Influence of Culture, Transparency, Trust, and Degree of Automation on Automation Use

Abstract

The reported study compares groups of 120 participants each, from the United States, Taiwan, and Turkey interacting with versions of an automated path planner that vary in transparency and degree of automation. The nationalities were selected in accordance with the theory of Cultural Syndromes as representatives of Dignity (US), Face (Taiwan), and Honor (Turkey) cultures and were predicted to differ in readiness to trust automation, degree of transparency required to use automation, and willingness to use systems with high degrees of automation. Three experimental conditions were tested. In the first, highlight, path conflicts were highlighted leaving rerouting to the participant. In the second, re-planner made requests for permission to reroute when a path conflict was detected. The third combined condition increased transparency of the re-planner by combining highlighting with rerouting to make the conflict on which decision was based visible to the user. A novel framework relating transparency, stages of automation, and trust in automation is proposed in which transparency plays a primary role in decisions to use automation but is supplemented by trust where there is insufficient information otherwise. Hypothesized cultural effects and framework predictions were confirmed.

Assessment of Trust in Automation in the “Real World”: Requirements for New Trust in Automation Measurement Techniques for Use by Practitioners

“…This breadth of domains and environments is matched with a similar depth into various individual and group level differences through examining the effects of age (McBride et al, 2010), gender (Pak et al, 2014), personality (Rupp et al, 2018), expertise (Madhavan & Wiegmann, 2007a), culture (Chien et al, 2019), working memory capacity (Rovira et al, 2017) and genetics (Parasuraman et al, 2012) on user trust. As a result, theories and models have been developed which not only predict a user’s level of trust but also how trust interacts with other factors (e.g., workload, SA) to influence human behavior (Chiou & Lee, 2021; de Visser et al, 2020; de Visser, Pak, et al, 2018b; Ghazizadeh et al, 2012; Hoff & Bashir, 2015).…”

Human–Autonomy Teaming: Definitions, Debates, and Directions

“…Where the conditions determining a partner's actions are not evident, however, attributions involving capabilities, objective functions (goals), or other characteristics of the machine may be needed to establish a basis for cooperation. For example, in Chien et al (2020), compliance doubled when participants were shown why a path planner wanted to re-route a UAV while at the same time an earlier correlation between trust and compliance was eliminated.…”

Trust, Shared Understanding and Locus of Control in Mixed-Initiative Robotic Systems

“…Most recent HRI research suggests that individual differences in trust might stem from whether the robot is perceived as an advanced tool, a human-like teammate [11], and/or by cultural differences [12]. Robot errors are found to have a profound and lasting effect on trust [13].…”