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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 121130 of 971 papers

TitleStatusHype
Automated Explanation Selection for Scientific Discovery0
Automated facial recognition system using deep learning for pain assessment in adults with cerebral palsy0
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence0
Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators0
A survey on Concept-based Approaches For Model Improvement0
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment0
A Complete Characterisation of ReLU-Invariant Distributions0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models0
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