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The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study
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A2: Although it is generally believed that the explanatory nature of the model helps to improve user trust, the experimental results show that this enhancement is not significant and not as effective as feedback. In specific cases, such as areas of low expertise, some form of interpretation may result in only a modest increase in appropriate trust.
三年前的除夕夜，一场意外车祸改变了他们所有人的命运曾经前途远大的青年，曾经相亲相爱的三口之家，曾经自信独立的女医生……但一切，都因为小女孩豆豆的死而改变
To assess trust more accurately, the researchers used behavioral trust (WoA), a measure that takes into account the difference between the user's predictions and the AI's recommendations, and is independent of the model's accuracy. By comparing WoA under different conditions, researchers can analyze the relationship between trust and performance.
The results show that feedback has a more significant impact on improving users' trust in AI than explainability, but this enhanced trust does not lead to a corresponding performance improvement. Further exploration suggests that feedback induces users to over-trust (i.e., accept the AI's suggestions when it is wrong) or distrust (ignore the AI's suggestions when it is correct), which may negate the benefits of increased trust, leading to a "trust-performance paradox". The researchers call for future research to focus on how to design strategies to ensure that explanations foster appropriate trust to improve the efficiency of human-robot collaboration.
2019 剧情 The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study | Xue Zhirong's knowledge base
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Q1: How does feedback affect users' trust in AI?
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Q3: How does result feedback and model interpretability affect user task performance?
A2: Although it is generally believed that the explanatory nature of the model helps to improve user trust, the experimental results show that this enhancement is not significant and not as effective as feedback. In specific cases, such as areas of low expertise, some form of interpretation may result in only a modest increase in appropriate trust.
三年前的除夕夜，一场意外车祸改变了他们所有人的命运曾经前途远大的青年，曾经相亲相爱的三口之家，曾经自信独立的女医生……但一切，都因为小女孩豆豆的死而改变
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Based on large language model generation, there may be a risk of errors.

The researchers conducted two sets of experiments ("Predict the speed-dating outcomes and get up to $6 (takes less than 20 min)" and a similar Prolific experiment) in which participants interacted with the AI system in a task of predicting the outcome of a dating to explore the impact of model explainability and feedback on user trust in AI and prediction accuracy. The results show that although explainability (e.g., global and local interpretation) does not significantly improve trust, feedback can most consistently and significantly improve behavioral trust. However, increased trust does not necessarily lead to the same level of performance gains, i.e., there is a "trust-performance paradox". Exploratory analysis reveals the mechanisms behind this phenomenon.

Based on large language model generation, there may be a risk of errors.
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The results show that feedback has a more significant impact on improving users' trust in AI than explainability, but this enhanced trust does not lead to a corresponding performance improvement. Further exploration suggests that feedback induces users to over-trust (i.e., accept the AI's suggestions when it is wrong) or distrust (ignore the AI's suggestions when it is correct), which may negate the benefits of increased trust, leading to a "trust-performance paradox". The researchers call for future research to focus on how to design strategies to ensure that explanations foster appropriate trust to improve the efficiency of human-robot collaboration.
2019 剧情
Dissertation Summary
主演: 范文芳,李铭顺,戚玉武

三年前的除夕夜，一场意外车祸改变了他们所有人的命运曾经前途远大的青年，曾经相亲相爱的三口之家，曾经自信独立的女医生……但一切，都因为小女孩豆豆的死而改变

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