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TAG: 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. 总消费:100.00 

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. 博学网络工作室 四川省成都市Based on large language model generation, there may be a risk of errors.超级版主版块理事人
担忧不会清空明日的烦恼,它只会丧失今日的勇气
- artificial intelligence
271 - Interactive
9 - 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.
34 - The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study | Xue Zhirong's knowledge base
12 - Q3: How does result feedback and model interpretability affect user task performance?
17 - Problem finding
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Draw inferences
- Q3: How does result feedback and model interpretability affect user task performance?
- Personal insights
- Draw inferences
- The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study
- 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.
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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.