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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 711720 of 971 papers

TitleStatusHype
An Experimentation Platform for Explainable Coalition Situational Understanding0
An Explainable AI Framework for Artificial Intelligence of Medical Things0
An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery0
An Explainable Artificial Intelligence Framework for Quality-Aware IoE Service Delivery0
An Explainable Fast Deep Neural Network for Emotion Recognition0
An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival0
A Novel Approach for Semiconductor Etching Process with Inductive Biases0
A Novel Explainable Artificial Intelligence Model in Image Classification problem0
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model0
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