Effects of Influence on User Trust in Predictive Decision Making

Zhou, Jianlong; Liu, Zhidong; Hu, Huaiwen; Yu, Kun; Chen, Fang; Li, Zelin; Wang, Yang

doi:10.1145/3290607.3312962

Cited by 18 publications

(11 citation statements)

References 7 publications

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“…Regarding the investigation of best explanations, some studies rely on user evaluation, namely users' subjective opinions expressed in surveys or interviews. Different types of measurements have been proposed, measuring user satisfaction [10], acceptance [11], trust [12] or the goodness of an explanation [13]. Miller et al [14] state that humans are more likely to accept explanations that are consistent with their prior beliefs.…”

Section: Introductionmentioning

confidence: 99%

Comparing User Perception of Explanations Developed with XAI Methods

Aechtner

Cabrera

Katwal

et al. 2022

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Section: Introductionmentioning

confidence: 99%

Comparing User Perception of Explanations Developed with XAI Methods

Aechtner

Cabrera

Katwal

et al. 2022

2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

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“…This finding is consistent with other work that focused on predictive decision making, where cognitive load was found to be affected by trust, reliance and the overall difficulty of the task. ( Alvarado-Valencia and Barrero, 2014 ; Zhou et al, 2019 ). To further explore which combinations of factors could predict trust ratings best, we performed several multi-linear regressions.…”

Section: Discussionmentioning

confidence: 99%

Inferring Trust From Users’ Behaviours; Agents’ Predictability Positively Affects Trust, Task Performance and Cognitive Load in Human-Agent Real-Time Collaboration

et al. 2021

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Collaborative virtual agents help human operators to perform tasks in real-time. For this collaboration to be effective, human operators must appropriately trust the agent(s) they are interacting with. Multiple factors influence trust, such as the context of interaction, prior experiences with automated systems and the quality of the help offered by agents in terms of its transparency and performance. Most of the literature on trust in automation identified the performance of the agent as a key factor influencing trust. However, other work has shown that the behavior of the agent, type of the agent’s errors, and predictability of the agent’s actions can influence the likelihood of the user’s reliance on the agent and efficiency of tasks completion. Our work focuses on how agents’ predictability affects cognitive load, performance and users’ trust in a real-time human-agent collaborative task. We used an interactive aiming task where participants had to collaborate with different agents that varied in terms of their predictability and performance. This setup uses behavioral information (such as task performance and reliance on the agent) as well as standardized survey instruments to estimate participants’ reported trust in the agent, cognitive load and perception of task difficulty. Thirty participants took part in our lab-based study. Our results showed that agents with more predictable behaviors have a more positive impact on task performance, reliance and trust while reducing cognitive workload. In addition, we investigated the human-agent trust relationship by creating models that could predict participants’ trust ratings using interaction data. We found that we could reliably estimate participants’ reported trust in the agents using information related to performance, task difficulty and reliance. This study provides insights on behavioral factors that are the most meaningful to anticipate complacent or distrusting attitudes toward automation. With this work, we seek to pave the way for the development of trust-aware agents capable of responding more appropriately to users by being able to monitor components of the human-agent relationships that are the most salient for trust calibration.

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“…Both the studies show that model accuracy and explanation fidelity or correctness influences user trust and that providing meaningless explanations harms user trust. In another study, Zhou et al [ 27 ] investigated the influence of ML explanation on user trust. Here, the explanations are presented by referring to training data points that influence predictions.…”

Section: Related Workmentioning

confidence: 99%