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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枕边书2015综艺观看至 2--期
中国新声代第一季综艺观看至 第2013-07-28期
绿色星球电影观看至 高清正片
永在我心间电影观看至 高清正片
爱的捆绑电影观看至 HD中字
星际传奇2电影观看至 正片
疯狂兰蛇电影观看至 HD
七娃捉妖记电影观看至 高清正片
我叫王土地（大河套）电影观看至 第1集
拯救阿塔里亚外星球电影观看至 正片
他她他她电影观看至 HD
江湖小龙侠电影观看至 高清正片
清除记录
Based on large language model generation, there may be a risk of errors.
枕边书2015综艺观看至 2--期
中国新声代第一季综艺观看至 第2013-07-28期
绿色星球电影观看至 高清正片
永在我心间电影观看至 高清正片
爱的捆绑电影观看至 HD中字
星际传奇2电影观看至 正片
疯狂兰蛇电影观看至 HD
七娃捉妖记电影观看至 高清正片
我叫王土地（大河套）电影观看至 第1集
拯救阿塔里亚外星球电影观看至 正片
他她他她电影观看至 HD
江湖小龙侠电影观看至 高清正片
清除记录
Conference
Q2: Does explainability necessarily enhance users' trust in AI?
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.
The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study
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The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study
The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study
Draw inferences
Q2: Does explainability necessarily enhance users' trust in AI?
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.
thesis
The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study
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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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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.