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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 551560 of 971 papers

TitleStatusHype
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification0
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME0
Metric Tools for Sensitivity Analysis with Applications to Neural Networks0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces0
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples0
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability0
Disagreement amongst counterfactual explanations: How transparency can be deceptive0
SketchXAI: A First Look at Explainability for Human Sketches0
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
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