Interview Based on large language model generation, there may be a risk of errors. 爱情片 喜剧片 犯罪片 悬疑片 恐怖片 剧情片 战争片 科幻片 纪录片

Interview The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study Translation Translation About me Interview solution The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study | Xue Zhirong's knowledge base thesis 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. Interactive Original address: Conference blog Problem finding The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study | Xue Zhirong's knowledge base MIT Licensed | Copyright © 2024-present Zhirong Xue's knowledge base The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study Robots and digital humans Based on large language model generation, there may be a risk of errors. 微电影 The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study interview 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. outcome Translation 网络电影

Interview Personal insights 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. Personal insights tool 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 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. 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. Dissertation Summary summary The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study 意大利 西班牙 加拿大 speech

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