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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 321330 of 971 papers

TitleStatusHype
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution0
Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence0
You can monitor your hydration level using your smartphone camera0
Beyond explaining: XAI-based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction0
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating0
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics0
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence0
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models0
Bridging Human Concepts and Computer Vision for Explainable Face Verification0
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