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Explainable artificial intelligence

XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Papers

Showing 151160 of 971 papers

TitleStatusHype
shapiq: Shapley Interactions for Machine LearningCode4
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme HeatwavesCode0
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare0
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data0
Leveraging CAM Algorithms for Explaining Medical Semantic SegmentationCode0
Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware0
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution0
Statistical tuning of artificial neural network0
Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition EstimationCode0
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing0
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