Measuring Trust of Human Operators in New Generation Rescue Robots

Park, Eui; Jenkins, Quaneisha; Jiang, Xiuping

doi:10.5739/isfp.2008.489

Cited by 25 publications

(19 citation statements)

References 11 publications

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“…By guiding the human operator's reliance, trust can influence human intention and behavior when coordinating with machines. It is defined as an attitudinal judgment of the extent to which the human operator can rely on the information obtained by automation to achieve their goals, particularly in situations involving risk and uncertainty (Lee & See, 2004;Freedy et al, 2007;Park, Jenkins & Jiang, 2008). Operators who trust a machine more tend to rely more heavily upon it, while individuals with low levels of trust in automation tend to rely less on automation (de Vries, Midden, & Bouwhuis, 2003;Lee & Moray, 1992;Merritt, 2011;Merritt & Ilgen, 2008;Wang, Jamieson, & Hollands, 2009).…”

Section: Trust In Machinementioning

confidence: 99%

Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected Event

Hsiung

Chiou

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after a surprise, or unexpected event. These biases further influence people’s trust in their partners, including machine partners (Muir, 1987; Madhavan & Wiegmann, 2004). Advances in robotics have allowed for robots to partner with people at work and be treated socially (Young, Hawkins, Sharlin & Igarashi, 2009). However, these advances may interfere with a person’s appropriate calibration of trust in robots (Parasuraman & Miller, 2004). A better understanding of attribution biases in the wake of an unexpected event may shed light on how trust develops in a robot partner. This study was built on a human coordination example to serve as a reference for future human-robot interactions. We posit that attribution biases lead people to blame their partner after experiencing a negative performance outcome, thus lowering their trust in the partner. Sixty participants (30 pairs) were tasked to coordinate with an unfamiliar human partner, to lift a 17.5 lb. box containing a 200ml cup of water filled to the brim, from the floor to a table, as quickly as possible without spilling water. Before the task, participants were told that the pair with the best performance would be rewarded; however, all pairs were told they did not achieve this. Participant pairs were randomly assigned to a surprise condition during which they heard a 250 Hz warning tone, or a baseline condition with no warning tone. Participants in both conditions were told to pause the task as quickly as possible if the warning tone was present. It was unknown to participants when or if a warning tone would occur. To assess participants’ trust in their partner, Muir’s (1987) trust questionnaire was administered twice, once after introducing the task to participants, and again after the coordination task was completed. To capture blame assignment, a scale based on Kim and Hinds (2006) was administered after participants were told they did not achieve the best performance. Results indicate participants were less likely to blame their partners for the negative outcome, compared to blaming themselves or the warning tone itself (in the surprise condition). Next, surprisingly, in the surprise condition, instead of experiencing a decrease of trust in a partner after the negative outcome, there was a significant increase in trust in their partners. No significant difference in trust was found in the baseline condition. Finally, results also indicate that initial trust in a partner is a significant predictor for how people assign blame. In general, the effects of attribution biases were not observed in the present study. Friendliness may be a factor in people’s assignment of blame; although participants were unfamiliar with one another, all participants were students at the same university. Second, shared experience during the surprise condition, including the chance to assess their partner’s behaviors in response to the warning tone, may have been a catalyst for increased trust in a partner. It is important to note that although physical differences between participants were not evaluated in this study, height may be a potential confounding factor in this task. These findings enlighten our understanding of physical human-robot coordination scenarios and trust in a partner.

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Section: Trust In Machinementioning

confidence: 99%

Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected Event

Hsiung

Chiou

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Lee and See [18] defined trust as "the attitude that an agent will help achieve an individual's goals in a situation characterised by uncertainty and vulnerability" (p. 54). In the context of human-automation teaming, trust can influence the willingness of humans to rely on the information obtained by an automated system, particularly in risky and uncertain environments [15,19]. Lack of trust will eventually lead the operator to intervene and take control [20].…”

Section: Trust In Industrial Robotsmentioning

confidence: 99%

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

Charalambous

2016

Advances in Intelligent Systems and Computing

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The concept of industrial human-robot collaboration (HRC) is becoming increasingly integrated into manufacturing production lines as a means for enhancing productivity and product quality. However, developments have focused primarily on the technology and, until recently, little research has been geared to understand the key human factors (HF) that need to be considered to enable successful implementation of industrial HRC. Recent work by the authors has led to the identification of key organisational and individual level HF. The purpose of this paper is to draw together the evidence from their studies and propose a HF roadmap for the successful implementation of industrial HRC. The roadmap will have profound implications as it enables automation specialists and manufacturing system engineers to understand the key HF that need to be considered optimise the efficiency and productivity of the collaboration between humans and industrial robots.

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“…Thus, the human may potentially fail to take advantage of the inherent robotic system benefits (Freedy, de Visser, Weltman, & Coeyman, 2007). For this reason, trust is especially critical when it comes to decision-making in high-risk environments, such as military combat missions (Park, Jenkins, & Jiang, 2008).…”

Section: The Relationship To Trustmentioning

confidence: 99%

Human-Animal Teams as an Analog for Future Human-Robot Teams: Influencing Design and Fostering Trust

Phillips

Schaefer

Billings³

et al. 2015

Journal of Human-Robot Interaction

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Our work posits that existing human-animal teams can serve as an analog for developing effective human-robot teams. Existing knowledge of human-animal partnerships can be readily applied to the HRI domain to foster accurate mental models and appropriately calibrated trust in future human-robot teams. Human-animal relationships are examined in terms of the benefiting roles animals can play in enabling effective teaming, as well as the level of team interdependency and team communication, with the goal of developing applications in future human-robot teams.

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Measuring Trust of Human Operators in New Generation Rescue Robots

Cited by 25 publications

References 11 publications

Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected Event

Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected Event

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

Human-Animal Teams as an Analog for Future Human-Robot Teams: Influencing Design and Fostering Trust

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