Results 1 to 10 of about 23,939 (267)
Journal of Perinatal Education, 2005 Childbirth educator humorously discusses props used as tools for teaching and teasing.
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Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI
CoRR, 2021 Many ML models are opaque to humans, producing decisions too complex for humans to easily understand. In response, explainable artificial intelligence (XAI) tools that analyze the inner workings of a model have been created. Despite these tools' strength in translating model behavior, critiques have raised concerns about the impact of XAI tools as a ...
Kiana Alikhademi +3 more
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Cognitive Science, 1994 Emotions and cognition are inextricably intertwined. Feelings influence thoughts and actions, which in turn can give rise to new emotional reactions. We claim that people infer emotional states in others using commonsense psychological theories of the interactions among emotions, cognition, and action.
Paul O'Rorke, Andrew Ortony
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Explainable Distance-Based Outlier Detection in Data Streams
IEEE Access, 2022 Explaining outliers is a topic that attracts a lot of interest; however existing proposals focus on the identification of the relevant dimensions. We extend this rationale for unsupervised distance-based outlier detection, and through investigating ...
Theodoros Toliopoulos +1 more
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Explaining visual counterfactual explainers
Trans. Mach. Learn. Res., 2023 Altres ajuts: this work was supported by the Generalitat de Catalunya under the Industrial Doctorate Program (grant number 2020DI62).
Velazquez Dorta, Diego Alejandro +5 more
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Explaining Simulations Through Self Explaining Agents [PDF]
Journal of Artificial Societies and Social Simulation, 2010 Several strategies are used to explain emergent interaction patterns in agent-based simulations. A distinction can be made between simulations in which the agents just behave in a reactive way, and simulations involving agents with also pro-active (goal-directed) behavior.
Maaike Harbers +2 more
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Frontiers in Computer ScienceAdapting to the addressee is crucial for successful explanations, yet poses significant challenges for dialog systems. We adopted the approach of treating explanation generation as a non-stationary decision process, in which the optimal strategy varies ...
Amelie S. Robrecht-Hilbig +5 more
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Diagnostics, 2022 To identify the best transfer learning approach for the identification of the most frequent abnormalities on chest radiographs (CXRs), we used embeddings extracted from pretrained convolutional neural networks (CNNs).
Noemi Gozzi +7 more
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Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic MethodsExplanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation".
Sergei Nirenburg +3 more
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What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach
BioengineeringThis article reflects on explainability in the context of medical artificial intelligence (AI) applications, focusing on AI-based clinical decision support systems (CDSS).
Elisabeth Hildt
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