新上线 热播榜 好评榜

全部类型 动作片 喜剧片 爱情片 科幻片 恐怖片 动画片 经典片

全部剧情 The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study 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. 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 Translation 科幻 thesis solution 警匪 犯罪 The content is made up of: Draw inferences Xue Zhirong's knowledge base About me 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 Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study | Xue Zhirong's knowledge base User experience Based on large language model generation, there may be a risk of errors. 经典 青春 MIT Licensed | Copyright © 2024-present Zhirong Xue's knowledge base 微电影 Q3: How does result feedback and model interpretability affect user task performance? Interview 记录 运动 speech outcome Robots and digital humans 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. blog 网络电影 summary

全部地区 artificial intelligence 香港 The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study 美国 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. 英国 Problem finding 韩国 Interactive Q1: How does feedback affect users' trust in AI? Conference 意大利 西班牙 加拿大 tool

全部语言 Personal insights 英语 粤语 闽南语 韩语 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. Q2: Does explainability necessarily enhance users' trust in AI? 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. summary

全部年份 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004

字母查找 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 0-9