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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 391400 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform0
Shapley values for cluster importance: How clusters of the training data affect a prediction0
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings0
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework0
Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases0
Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review0
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review0
Enhancing AI Transparency: XRL-Based Resource Management and RAN Slicing for 6G ORAN Architecture0
Enhanced Prototypical Part Network (EPPNet) For Explainable Image Classification Via Prototypes0
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