tool outcome blog

Personal insights speech artificial intelligence 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. interview The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study | Xue Zhirong's knowledge base summary Draw inferences

The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study Q3: How does result feedback and model interpretability affect user task performance? solution Robots and digital humans 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 Problem finding Translation Draw inferences thesis Interview 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. blog Q3: How does result feedback and model interpretability affect user task performance? Conference Xue Zhirong's knowledge base tool

The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study 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. blog The Interpretability of Artificial Intelligence and the Impact of Outcome Feedback on Trust: A Comparative Study Problem finding Robots and digital humans Q1: How does feedback affect users' trust in AI? Q1: How does feedback affect users' trust in AI? thesis tool tool

Draw inferences Personal insights 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. Xue Zhirong's knowledge base thesis Original address: 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. summary

Q3: How does result feedback and model interpretability affect user task performance? 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004

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 K AI XR HCI Ctrl WWDC G H I MiX Copilot K AI XR HCI Ctrl WWDC toggle sidebar R S T U V W X Y Z 0-9