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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 801810 of 971 papers

TitleStatusHype
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems0
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems0
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methodsCode1
Bounded logit attention: Learning to explain image classifiersCode0
Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI0
Explainable Activity Recognition for Smart Home Systems0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence0
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain0
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