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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 431440 of 971 papers

TitleStatusHype
Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP0
Explainable Interface for Human-Autonomy Teaming: A Survey0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification0
Explainable Label-flipping Attacks on Human Emotion Assessment System0
Concept-based Explainable Artificial Intelligence: A Survey0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand0
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines0
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?0
Explainable Machine Learning for Predicting Homicide Clearance in the United States0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis0
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