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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 111120 of 971 papers

TitleStatusHype
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks0
Attributions Beyond Neural Networks: The Linear Program Case0
A Turing Test for Transparency0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing0
A Survey on Explainable Artificial Intelligence for Cybersecurity0
Augmented cross-selling through explainable AI -- a case from energy retailing0
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications0
A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification0
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