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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 101110 of 971 papers

TitleStatusHype
An Argumentation-based Approach for Explaining Goal Selection in Intelligent Agents0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification0
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks0
A Survey on Explainable Artificial Intelligence for Cybersecurity0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification0
A Deep Generative XAI Framework for Natural Language Inference Explanations Generation0
A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare0
A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence0
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI0
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