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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 221230 of 971 papers

TitleStatusHype
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making0
Causal Explanations and XAI0
Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review0
Adapting the Biological SSVEP Response to Artificial Neural Networks0
Attributions Beyond Neural Networks: The Linear Program Case0
Deep Learning for predicting rate-induced tipping0
Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain0
Deep Learning Reproducibility and Explainable AI (XAI)0
Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations0
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