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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 471480 of 971 papers

TitleStatusHype
Natural Example-Based Explainability: a Survey0
A Context-Sensitive Approach to XAI in Music Performance0
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognitionCode0
Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care0
Leveraging Learning Metrics for Improved Federated Learning0
Prediction of Diblock Copolymer Morphology via Machine Learning0
Explanations for Answer Set ProgrammingCode0
Glocal Explanations of Expected Goal Models in SoccerCode0
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques0
Ensemble of Counterfactual ExplainersCode0
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