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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 901925 of 971 papers

TitleStatusHype
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood GenerationCode0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
Locality Guided Neural Networks for Explainable Artificial Intelligence0
Am I Building a White Box Agent or Interpreting a Black Box Agent?0
Drug discovery with explainable artificial intelligence0
Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels0
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
Does Explainable Artificial Intelligence Improve Human Decision-Making?0
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
Explanations of Black-Box Model Predictions by Contextual Importance and UtilityCode0
Explainable Artificial Intelligence: a Systematic Review0
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
Evolved Explainable Classifications for Lymph Node Metastases0
Explainable Reinforcement Learning: A Survey0
Post-hoc explanation of black-box classifiers using confident itemsets0
A multi-component framework for the analysis and design of explainable artificial intelligence0
LIMEtree: Consistent and Faithful Multi-class ExplanationsCode0
Explaining How Deep Neural Networks Forget by Deep VisualizationCode0
The Grammar of Interactive Explanatory Model AnalysisCode1
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review0
Foundations of Explainable Knowledge-Enabled Systems0
Learning to Structure an Image with Few ColorsCode1
Directions for Explainable Knowledge-Enabled Systems0
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