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湖南卫视新春点映《父子对对碰》The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study 观看至 2020-01-23
枕边书2015The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study 观看至 2--期
solutionThe Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study Xue Zhirong is a designer, engineer, and author of several books; Founder of the Design Open Source Community, Co-founder of MiX Copilot; Committed to making the world a better place with design and technology. This knowledge base will update AI, HCI and other content, including news, papers, presentations, sharing, etc.
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The researchers found that although it is generally believed that the interpretability of the model can help improve the user's trust in the AI system, in the actual experiment, the global and local interpretability does not lead to a stable and significant trust improvement. Conversely, feedback (i.e., the output of the results) has a more significant effect on increasing user trust in the AI. However, this increased trust does not directly translate into an equivalent improvement in performance.The content is made up of:观看至 高清正片
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林中小屋The content is made up of:观看至 高清正片
安娜的战争The content is made up of:观看至 高清正片
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湖南卫视新春点映《父子对对碰》The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study 观看至 2020-01-23
枕边书2015The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study 观看至 2--期
solutionThe Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study Xue Zhirong is a designer, engineer, and author of several books; Founder of the Design Open Source Community, Co-founder of MiX Copilot; Committed to making the world a better place with design and technology. This knowledge base will update AI, HCI and other content, including news, papers, presentations, sharing, etc.
绿色星球The content is made up of:观看至 高清正片
The researchers found that although it is generally believed that the interpretability of the model can help improve the user's trust in the AI system, in the actual experiment, the global and local interpretability does not lead to a stable and significant trust improvement. Conversely, feedback (i.e., the output of the results) has a more significant effect on increasing user trust in the AI. However, this increased trust does not directly translate into an equivalent improvement in performance.The content is made up of:观看至 高清正片
爱的捆绑The content is made up of:观看至 HD中字
星际传奇2The content is made up of:观看至 正片
疯狂兰蛇The content is made up of:观看至 HD
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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.
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.
赌徒高野继承千万资产后输得一干二净，家道也因此中落，就在他一筹莫展时，打扮妖冶的堕落天使茉茉主动上门提供特殊服务不料他们却遭遇庄家买凶追杀，蒙眼飙车、女儿被绑一系列意外接踵而至嗜赌成性的赌徒和用爱洗白的风情女，天亮之前他们该如何选择接下来的路
Based on large language model generation, there may be a risk of errors.
2016 爱情,犯罪
更新:
2024-05-22 09:00:01
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.
Q3: How does result feedback and model interpretability affect user task performance?
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.
赌徒高野继承千万资产后输得一干二净，家道也因此中落，就在他一筹莫展时，打扮妖冶的堕落天使茉茉主动上门提供特殊服务不料他们却遭遇庄家买凶追杀，蒙眼飙车、女儿被绑一系列意外接踵而至嗜赌成性的赌徒和用爱洗白的风情女，天亮之前他们该如何选择接下来的路
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.
2016 爱情,犯罪
导演: 吴中天,
主演: 郭富城,杨子姗,郝蕾,安志杰,高捷,
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.
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