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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 751760 of 971 papers

TitleStatusHype
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI0
Flood Prediction and Analysis on the Relevance of Features using Explainable Artificial Intelligence0
Explainable AI Integrated Feature Selection for Landslide Susceptibility Mapping using TreeSHAP0
An Accelerator for Rule Induction in Fuzzy Rough TheoryCode0
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings0
A Critical Review of Inductive Logic Programming Techniques for Explainable AI0
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AICode0
Towards a Shapley Value Graph Framework for Medical peer-influence0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence0
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?0
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