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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 691700 of 971 papers

TitleStatusHype
GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping0
GLIME: A new graphical methodology for interpretable model-agnostic explanations0
Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models0
GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes0
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning0
GPU-Accelerated Rule Evaluation and Evolution0
GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network0
Guarding Digital Privacy: Exploring User Profiling and Security Enhancements0
Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models0
Hardware Acceleration of Explainable Artificial Intelligence0
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