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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 871880 of 971 papers

TitleStatusHype
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution0
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis0
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis0
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing0
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios0
Evaluating quantum generative models via imbalanced data classification benchmarks0
Evaluating the Explainable AI Method Grad-CAM for Breath Classification on Newborn Time Series Data0
Assessing the Efficacy of Deep Learning Approaches for Facial Expression Recognition in Individuals with Intellectual Disabilities0
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability0
Evaluation of Human-Understandability of Global Model Explanations using Decision Tree0
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