User Trust in Intelligent Systems: A Journey Over Time

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “[162] entitled ‘‘User Trust in Intelligent Systems: A Journey Over Time is work’’, confirmed based on experimental studies that explanation impact trust.”
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning (2019, background): “computer interaction (HCI) researchers actively explore this area [1, 14], and propose several metrics for human evaluation on general explanation from IML techniques, such as mental mode [20], human-machine performance [9], user satisfaction [19] and user trust [15].”
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies (2020, background): “Other important influences at play are perceived system ability, control, and predictability [82].”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “the system over time with continued interaction with the system. This brings the importance of repeated temporal data capture (in contrast to static measurements) for XAI evaluations. Holliday et al. [89] present an example of multiple trust assessments during the user study. They measured user trus”