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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 821830 of 971 papers

TitleStatusHype
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in HistopathologyCode0
Do Not Trust Additive ExplanationsCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
On the Explanation of Machine Learning Predictions in Clinical Gait AnalysisCode0
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI SystemsCode0
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanationsCode0
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model ConversationsCode0
Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidentsCode0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
Visual Interpretability for Deep Learning: a SurveyCode0
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