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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 401410 of 971 papers

TitleStatusHype
Shapley values for cluster importance: How clusters of the training data affect a prediction0
Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction0
Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer0
Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network0
Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform0
Concept Embedding Analysis: A Review0
Assessing high-order effects in feature importance via predictability decomposition0
Explainable Artificial Intelligence (XAI): An Engineering Perspective0
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine0
A multi-component framework for the analysis and design of explainable artificial intelligence0
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