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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 791800 of 971 papers

TitleStatusHype
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?Code0
A Turing Test for Transparency0
Rational Shapley ValuesCode0
Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactions0
Exploring deterministic frequency deviations with explainable AICode0
Counterfactual Explanations as Interventions in Latent SpaceCode0
Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI0
An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray ImagesCode0
Entropy-based Logic Explanations of Neural NetworksCode1
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features0
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