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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 851860 of 971 papers

TitleStatusHype
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence0
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer0
Does Explainable Artificial Intelligence Improve Human Decision-Making?0
Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type0
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response0
Drug discovery with explainable artificial intelligence0
Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare0
ECLAD: Extracting Concepts with Local Aggregated Descriptors0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems0
Efficient XAI Techniques: A Taxonomic Survey0
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