Probabilistic Human-Computer Trust Handling

Nothdurft, Florian; Richter, Felix; Minker, Wolfgang

doi:10.3115/v1/w14-4307

Cited by 31 publications

(10 citation statements)

References 14 publications

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“…This fulfills Yorke-Smith's guideline to enhance the proactive system's understandability. Besides, justification explanations have shown to improve the user's trust in automatic systems [61]. Based on these considerations, we obtain four levels of proactivity:…”

Section: Formalisation Of Proactive Dialoguementioning

confidence: 99%

The Role of Trust in Proactive Conversational Assistants

et al. 2021

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Humans and machines harmoniously collaborating and benefiting from each other is a long lasting dream for researchers in robotics and artificial intelligence. An important feature of efficient and rewarding cooperation is the ability to assume possible problematic situations and act in advance to prevent negative outcomes. This concept of assistance is known under the term proactivity. In this article, we investigate the development and implementation of proactive dialogues for fostering a trustworthy humancomputer relationship and providing adequate and timely assistance. Here, we make several contributions. A formalisation of proactive dialogue in conversational assistants is provided. The formalisation forms a framework for integrating proactive dialogue in conversational applications. Additionally, we present a study showing the relations between proactive dialogue actions and several aspects of the perceived trustworthiness of a system as well as effects on the user experience. The results of the experiments provide significant contributions to the line of proactive dialogue research. Particularly, we provide insights on the effects of proactive dialogue on the human-computer trust relationship and dependencies between proactive dialogue and user specific and situational characteristics.

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Section: Formalisation Of Proactive Dialoguementioning

confidence: 99%

The Role of Trust in Proactive Conversational Assistants

et al. 2021

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“…Accordingly, trust in automation as well is not a simple variable but rather a complex multidisciplinary context dependent construct that consists of many different dimensions (Yagoda and Gillan, 2012). This also applies for many other fields where trust has an influence on the whole interaction outcome [e.g., in technology (Gulati et al, 2017), in computers (Nothdurft et al, 2014), in automation Hoff and Bashir, 2015;John D.), and in robots (Hancock et al, 2011;Yagoda and Gillan, 2012)].…”

Section: Modeling Trust and Its Factorsmentioning

confidence: 99%

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Alhaji

Prilla

Rausch

2021

Front. Artif. Intell.

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Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot’s reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot’s feedback accuracy has a conditional effect on trust depending on the robot’s reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant.

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“…Nonetheless, the human's mental model evolves after interaction with the robot. A mismatch between the human's initial mental model and the later mental model can have a detrimental effect on the human's trust [38]. A human's mental model of a robot's functional and behavioural abilities also defines the human's intentions for future use of robot [8].…”

Section: Related Workmentioning

confidence: 99%

Explanations from a Robotic Partner Build Trust on the Robot’s Decisions for Collaborative Human-Humanoid Interaction

Javaid

Estivill‐Castro

2021

Robotics

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Typically, humans interact with a humanoid robot with apprehension. This lack of trust can seriously affect the effectiveness of a team of robots and humans. We can create effective interactions that generate trust by augmenting robots with an explanation capability. The explanations provide justification and transparency to the robot’s decisions. To demonstrate such effective interaction, we tested this with an interactive, game-playing environment with partial information that requires team collaboration, using a game called Spanish Domino. We partner a robot with a human to form a pair, and this team opposes a team of two humans. We performed a user study with sixty-three human participants in different settings, investigating the effect of the robot’s explanations on the humans’ trust and perception of the robot’s behaviour. Our explanation-generation mechanism produces natural-language sentences that translate the decision taken by the robot into human-understandable terms. We video-recorded all interactions to analyse factors such as the participants’ relational behaviours with the robot, and we also used questionnaires to measure the participants’ explicit trust in the robot. Overall, our main results demonstrate that explanations enhanced the participants’ understandability of the robot’s decisions, because we observed a significant increase in the participants’ level of trust in their robotic partner. These results suggest that explanations, stating the reason(s) for a decision, combined with the transparency of the decision-making process, facilitate collaborative human–humanoid interactions.

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Probabilistic Human-Computer Trust Handling

Cited by 31 publications

References 14 publications

The Role of Trust in Proactive Conversational Assistants

The Role of Trust in Proactive Conversational Assistants

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Explanations from a Robotic Partner Build Trust on the Robot’s Decisions for Collaborative Human-Humanoid Interaction

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