[HTML][HTML] Leveraging explainable AI for informed building retrofit decisions: Insights from a survey
D Leuthe, J Mirlach, S Wenninger, C Wiethe - Energy and buildings, 2024 - Elsevier
Accurate predictions of building energy consumption are essential for reducing the energy
performance gap. While data-driven energy quantification methods based on machine …
performance gap. While data-driven energy quantification methods based on machine …
Eye tracking insights into physician behaviour with safe and unsafe explainable AI recommendations
We studied clinical AI-supported decision-making as an example of a high-stakes setting in
which explainable AI (XAI) has been proposed as useful (by theoretically providing …
which explainable AI (XAI) has been proposed as useful (by theoretically providing …
Explanations considered harmful: the impact of misleading explanations on accuracy in hybrid human-ai decision making
Explainable AI (XAI) has the potential to enhance decision-making in human-AI
collaborations, yet existing research indicates that explanations can also lead to undue …
collaborations, yet existing research indicates that explanations can also lead to undue …
Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making
AI systems are used in many domains to assist with decision making, and although the
potential for AI systems to assist with decision making is much discussed, human-AI …
potential for AI systems to assist with decision making is much discussed, human-AI …
Human-AI collaboration: Unraveling the effects of user proficiency and AI agent capability in intelligent decision support systems
L Peng, D Li, Z Zhang, T Zhang, A Huang… - International Journal of …, 2024 - Elsevier
Artificial intelligence (AI) agents are integral components of modern intelligent decision
support systems (IDSS), providing their capability to assist in decision-making processes …
support systems (IDSS), providing their capability to assist in decision-making processes …
VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-Making
Ensuring that Machine Learning (ML) models make correct and meaningful inferences is
necessary for the broader adoption of such models into high-stakes decision-making …
necessary for the broader adoption of such models into high-stakes decision-making …
Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting
The growing capabilities of AI models are leading to their wider use, including in safety-
critical domains. Explainable AI (XAI) aims to make these models safer to use by making …
critical domains. Explainable AI (XAI) aims to make these models safer to use by making …
Evaluating the Influences of Explanation Style on Human-AI Reliance
E Casolin, FD Salim, B Newell - arXiv preprint arXiv:2410.20067, 2024 - arxiv.org
Explainable AI (XAI) aims to support appropriate human-AI reliance by increasing the
interpretability of complex model decisions. Despite the proliferation of proposed methods …
interpretability of complex model decisions. Despite the proliferation of proposed methods …
(X) AI as a Teacher: Learning with Explainable Artificial Intelligence
Due to changing demographics, limited availability of experts, and frequent job transitions,
retaining and sharing knowledge within organizations is crucial. While many learning …
retaining and sharing knowledge within organizations is crucial. While many learning …
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration
Across various applications, humans increasingly use black-box artificial intelligence (AI)
systems without insight into these systems' reasoning. To counter this opacity, explainable AI …
systems without insight into these systems' reasoning. To counter this opacity, explainable AI …