Individual Differences in Trust in Autonomous Robots: Implications for Transparency

Matthews, Gerald; Lin, Jie; Panganiban, April Rose; Long, Michael D.

doi:10.1109/thms.2019.2947592

Cited by 59 publications

(46 citation statements)

References 52 publications

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“…Generally, explanations contribute to transparency; as it is defined as the provision of information to help the human understand various aspects of agent functioning [27]. A recent study suggests that transparency should be compatible with the user's mental model of the system in order to support accurate trust calibration [31]. A mental model is an internal representation in the mind of one actor about the characteristics of another actor [59].…”

Section: Discussionmentioning

confidence: 99%

“…Different forms of transparency might be needed dependent on whether the humans representation of the system concerns an advanced tool or a teammate. Accordingly, personalized feedback that highlights either the machine's data-analytic capabilities (advanced tool) or its humanlike social functioning (teammate) provides a strategy for trust management [31]. In that sense, an explanation is far more complex than an expression of regret, as there is a wider range of possible underlying messages of the explanation and the way they are articulated.…”

Section: Discussionmentioning

confidence: 99%

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Trust repair in human-agent teams: the effectiveness of explanations and expressing regret

Kox

Kerstholt

Hueting³

et al. 2021

Auton Agent Multi-Agent Syst

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The role of intelligent agents becomes more social as they are expected to act in direct interaction, involvement and/or interdependency with humans and other artificial entities, as in Human-Agent Teams (HAT). The highly interdependent and dynamic nature of teamwork demands correctly calibrated trust among team members. Trust violations are an inevitable aspect of the cycle of trust and since repairing damaged trust proves to be more difficult than building trust initially, effective trust repair strategies are needed to ensure durable and successful team performance. The aim of this study was to explore the effectiveness of different trust repair strategies from an intelligent agent by measuring the development of human trust and advice taking in a Human-Agent Teaming task. Data for this study were obtained using a task environment resembling a first-person shooter game. Participants carried out a mission in collaboration with their artificial team member. A trust violation was provoked when the agent failed to detect an approaching enemy. After this, the agent offered one of four trust repair strategies, composed of the apology components explanation and expression of regret (either one alone, both or neither). Our results indicated that expressing regret was crucial for effective trust repair. After trust declined due to the violation by the agent, trust only significantly recovered when an expression of regret was included in the apology. This effect was stronger when an explanation was added. In this context, the intelligent agent was the most effective in its attempt of rebuilding trust when it provided an apology that was both affective, and informational. Finally, the implications of our findings for the design and study of Human-Agent trust repair are discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Trust repair in human-agent teams: the effectiveness of explanations and expressing regret

Kox

Kerstholt

Hueting³

et al. 2021

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

show abstract

“…The variable perceived comprehensibility is a novel trust antecedent that has become increasingly important since humans interact with intelligent and autonomous systems (Matthews et al 2020)as the efforts of governments and companies to explainable AI underline (Cutler, Pribi c, and Humphrey 2019;Fjeld and Nagy 2020). The effect on trust is not as strong as the one of perceived ability: when perceived comprehensibility increases by one standard deviation, trust rises 0.174 standard deviationsassuming all other antecedents stay constant.…”

Section: Discussionmentioning

confidence: 99%

“…Similar to the trust model for interaction with an intelligent, autonomous robot of Matthews et al (2020), we assumed that not all of our six antecedents directly influence the trust, but get mediated through other antecedents. Thus, we employed the (probably) most generic expression of a mediation model (Baron and Kenny 1986), the SOR model of Woodworth (1926), to structure our antecedents.…”

Section: Hypotheses Developmentmentioning

confidence: 99%

Trust in artificial intelligence within production management – an exploration of antecedents

et al. 2021

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Industry 4.0, big data, predictive analytics, and robotics are leading to a paradigm shift on the shop floor of industrial production. However, complex, cognitive tasks are also subject of change, due to the development of artificial intelligence (AI). Smart assistants are finding their way into the world of knowledge work and require cooperation with humans. Here, trust is an essential factor that determines the success of human-AI cooperation. Within this article, an analysis within production management identifies possible antecedent variables on trust in AI and evaluates these due to interaction scenarios with AI. The results of this research are five antecedents for human trust in AI within production management. From these results, preliminary design guidelines are derived for a socially sustainable human-AI interaction in future production management systems.

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“…Therefore, the presentation of robots to users (i.e. perceived design features) can also have an impact on whether the public trusts the system or not [37,38]. The next section discusses the trusting intentions towards robots.…”

Section: Intelligent Agent Definitionmentioning

confidence: 99%

Trusting Intentions Towards Robots in Healthcare: A Theoretical Framework

O’Connor

Kupper²,

Heavin

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Within the next decade, robots (intelligent agents that are able to perform tasks normally requiring human intelligence) may become more popular when delivering healthcare services to patients. The use of robots in this way may be daunting for some members of the public, who may not understand this technology and deem it untrustworthy. Others may be excited to use and trust robots to support their healthcare needs. It is argued that (1) context plays an integral role in Information Systems (IS) research and (2) technology demonstrating anthropomorphic or system-like features impact the extent to which an individual trusts the technology. Yet, there is little research which integrates these two concepts within one study in healthcare. To address this gap, we develop a theoretical framework that considers trusting intentions towards robots based on the interaction of humans and robots within the contextual landscape of delivering healthcare services. This article presents a theory-based approach to developing effective trustworthy intelligent agents at the intersection of IS and Healthcare.

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Individual Differences in Trust in Autonomous Robots: Implications for Transparency

Cited by 59 publications

References 52 publications

Trust repair in human-agent teams: the effectiveness of explanations and expressing regret

Trust repair in human-agent teams: the effectiveness of explanations and expressing regret

Trust in artificial intelligence within production management – an exploration of antecedents

Trusting Intentions Towards Robots in Healthcare: A Theoretical Framework

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