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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 131140 of 971 papers

TitleStatusHype
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis0
Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness0
Explainable AI-Based Interface System for Weather Forecasting Model0
Which LIME should I trust? Concepts, Challenges, and Solutions0
Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance0
Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond0
Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI0
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays0
Logic Explanation of AI Classifiers by Categorical Explaining Functors0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures0
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