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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 121130 of 971 papers

TitleStatusHype
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2Code1
Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes0
Explainable Artificial Intelligence for Medical Applications: A Review0
Adapting the Biological SSVEP Response to Artificial Neural Networks0
X-DFS: Explainable Artificial Intelligence Guided Design-for-Security Solution Space Exploration0
BayesNAM: Leveraging Inconsistency for Reliable Explanations0
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review0
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection0
Causal Discovery and Classification Using Lempel-Ziv ComplexityCode0
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing0
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