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电视剧陆剧韩剧 Xue Zhirong's knowledge base Conference Conference 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. About me Draw inferences

The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study | Xue Zhirong's knowledge base

interview 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 Dissertation Summary 都市 thesis Problem finding Translation Translation 青春偶像 The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study User experience Q3: How does result feedback and model interpretability affect user task performance? thesis artificial intelligence User experience The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study Interactive Interview About me Problem finding tool solution Q3: How does result feedback and model interpretability affect user task performance? MIT Licensed | Copyright © 2024-present Zhirong Xue's knowledge base speech

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interview 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 韩国香港 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. Robots and digital humans 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. The content is made up of: 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. 新加坡 speech

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

interview artificial intelligence 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. Dissertation Summary 闽南语韩语 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. outcome

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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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. summary 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.

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