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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 451460 of 971 papers

TitleStatusHype
Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective0
Medical Image Denosing via Explainable AI Feature Preserving Loss0
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions0
Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification0
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning0
XAI Benchmark for Visual Explanation0
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research0
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation LearningCode0
aSAGA: Automatic Sleep Analysis with Gray Areas0
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