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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 381390 of 971 papers

TitleStatusHype
Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models0
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions0
Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models0
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand0
Concept-based Explainable Artificial Intelligence: A Survey0
Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP0
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey0
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification0
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