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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 421430 of 971 papers

TitleStatusHype
Counterfactual Formulation of Patient-Specific Root Causes of Disease0
Explainable Deep Image Classifiers for Skin Lesion Diagnosis0
Explainable Deep Learning Framework for Human Activity Recognition0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges0
A general approach to compute the relevance of middle-level input features0
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification0
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review0
Explainable Multi-Label Classification of MBTI Types0
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