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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 851860 of 971 papers

TitleStatusHype
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research0
VitrAI -- Applying Explainable AI in the Real World0
Principles of Explanation in Human-AI Systems0
Mitigating belief projection in explainable artificial intelligence via Bayesian TeachingCode0
Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders0
Convolutional Neural Network Interpretability with General Pattern Theory0
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond0
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks0
Hierarchical Variational Autoencoder for Visual Counterfactuals0
Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence0
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