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The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study
A1: According to research, feedback (e.g. result output) is a key factor influencing user trust. It is the most significant and reliable way to increase user trust in AI behavior.
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The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study
武神主宰
A3: The study found that the feedback of the results can improve the accuracy of the user's predictions (reducing the absolute error), thereby improving the performance of working with AI. However, interpretability does not have as much impact on user task performance as it does on trust. This may mean that we should pay more attention to how to effectively use feedback mechanisms to improve the usefulness and effectiveness of AI-assisted decision-making.
主角秦尘本是武域中最顶尖的天才强者，却遭歹人暗算，陨落大陆禁地死亡峡谷。必死无疑的秦尘，却意外触发神秘古剑的力量…… 三百年后，天武大陆偏僻之地，一位同名少年意外继承了秦尘的意志。作为大齐国军神定武王的爱孙，却因生父来历成迷，母子二人在定武王府中受尽冷遇，相依为命。 为了重写望日的强者神话，也为了守护自己所爱的一切，秦尘毅然决然扛起维护天下五国的大任，再度踏上武道之路。
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Q3: How does result feedback and model interpretability affect user task performance?
2020 动画,奇幻,冒险 更新: 2024-08-03 21:59:43
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artificial intelligence
A3: The study found that the feedback of the results can improve the accuracy of the user's predictions (reducing the absolute error), thereby improving the performance of working with AI. However, interpretability does not have as much impact on user task performance as it does on trust. This may mean that we should pay more attention to how to effectively use feedback mechanisms to improve the usefulness and effectiveness of AI-assisted decision-making.
主角秦尘本是武域中最顶尖的天才强者，却遭歹人暗算，陨落大陆禁地死亡峡谷。必死无疑的秦尘，却意外触发神秘古剑的力量…… 三百年后，天武大陆偏僻之地，一位同名少年意外继承了秦尘的意志。作为大齐国军神定武王的爱孙，却因生父来历成迷，母子二人在定武王府中受尽冷遇，相依为命。 为了重写望日的强者神话，也为了守护自己所爱的一切，秦尘毅然决然扛起维护天下五国的大任，再度踏上武道之路。
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Based on large language model generation, there may be a risk of errors.

Interview

Based on large language model generation, there may be a risk of errors.
summary

Interview

summary
Based on large language model generation, there may be a risk of errors.

Q1: How does feedback affect users' trust in AI?

Based on large language model generation, there may be a risk of errors.
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.

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.

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.
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Interview

Translation
Based on large language model generation, there may be a risk of errors.

Q2: Does explainability necessarily enhance users' trust in AI?

Based on large language model generation, there may be a risk of errors.
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Q1: How does feedback affect users' trust in AI?

Xue Zhirong's knowledge base
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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.

Interview

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.
Q2: Does explainability necessarily enhance users' trust in AI?

Interview

Q2: Does explainability necessarily enhance users' trust in AI?
A3: The study found that the feedback of the results can improve the accuracy of the user's predictions (reducing the absolute error), thereby improving the performance of working with AI. However, interpretability does not have as much impact on user task performance as it does on trust. This may mean that we should pay more attention to how to effectively use feedback mechanisms to improve the usefulness and effectiveness of AI-assisted decision-making.

Interview

A3: The study found that the feedback of the results can improve the accuracy of the user's predictions (reducing the absolute error), thereby improving the performance of working with AI. However, interpretability does not have as much impact on user task performance as it does on trust. This may mean that we should pay more attention to how to effectively use feedback mechanisms to improve the usefulness and effectiveness of AI-assisted decision-making.
A3: The study found that the feedback of the results can improve the accuracy of the user's predictions (reducing the absolute error), thereby improving the performance of working with AI. However, interpretability does not have as much impact on user task performance as it does on trust. This may mean that we should pay more attention to how to effectively use feedback mechanisms to improve the usefulness and effectiveness of AI-assisted decision-making.

Interview

A3: The study found that the feedback of the results can improve the accuracy of the user's predictions (reducing the absolute error), thereby improving the performance of working with AI. However, interpretability does not have as much impact on user task performance as it does on trust. This may mean that we should pay more attention to how to effectively use feedback mechanisms to improve the usefulness and effectiveness of AI-assisted decision-making.
Q3: How does result feedback and model interpretability affect user task performance?
2020 动画,奇幻,冒险
导演: 老鹰
主演: 许子尧,唐泽宗,陈帅,吴凡,孙科

主角秦尘本是武域中最顶尖的天才强者，却遭歹人暗算，陨落大陆禁地死亡峡谷。必死无疑的秦尘，却意外触发神秘古剑的力量…… 三百年后，天武大陆偏僻之地，一位同名少年意外继承了秦尘的意志。作为大齐国军神定武王的爱孙，却因生父来历成迷，母子二人在定武王府中受尽冷遇，相依为命。 为了重写望日的强者神话，也为了守护自己所爱的一切，秦尘毅然决然扛起维护天下五国的大任，再度踏上武道之路。

English
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.
Xue Zhirong, Designer, Interaction Design, Human-Computer Interaction, Artificial Intelligence, Official Website, Blog, Creator, Author, Engineer, Paper, Product Design, Research, AI, HCI, Design, Learning, Knowledge Base, xuezhirong, UX, Design, Research, AI, HCI, Designer, Engineer, Author, Blog, Papers, Product Design, Study, Learning, User Experience
Xue Zhirong, Designer, Interaction Design, Human-Computer Interaction, Artificial Intelligence, Official Website, Blog, Creator, Author, Engineer, Paper, Product Design, Research, AI, HCI, Design, Learning, Knowledge Base, xuezhirong, UX, Design, Research, AI, HCI, Designer, Engineer, Author, Blog, Papers, Product Design, Study, Learning, User Experience
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