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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 110 of 971 papers

TitleStatusHype
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
shapiq: Shapley Interactions for Machine LearningCode4
Adversarial attacks and defenses in explainable artificial intelligence: A surveyCode2
PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and ModelsCode2
Xplique: A Deep Learning Explainability ToolboxCode2
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
Explainable AI in Spatial AnalysisCode2
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
Automatic Extraction of Linguistic Description from Fuzzy Rule BaseCode1
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy SensorsCode1
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