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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 671680 of 971 papers

TitleStatusHype
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion0
Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders0
AcME-AD: Accelerated Model Explanations for Anomaly Detection0
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI0
A Complete Characterisation of ReLU-Invariant Distributions0
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks0
A Context-Sensitive Approach to XAI in Music Performance0
A Critical Review of Inductive Logic Programming Techniques for Explainable AI0
Adapting the Biological SSVEP Response to Artificial Neural Networks0
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