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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 591600 of 971 papers

TitleStatusHype
Backtracking Counterfactuals0
Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence0
A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence0
XAI for transparent wind turbine power curve modelsCode1
Sustainable Personalisation and Explainability in Dyadic Data Systems0
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide PredictionCode0
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust PerspectiveCode0
Explaining machine learning models for age classification in human gait analysis0
Explaining automated gender classification of human gait0
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