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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 651660 of 971 papers

TitleStatusHype
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI0
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models0
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability0
Visualizing and Understanding Contrastive LearningCode0
Eliminating The Impossible, Whatever Remains Must Be TrueCode0
Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches0
Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species AnnotationsCode1
Attributions Beyond Neural Networks: The Linear Program Case0
Explainable expected goal models for performance analysis in football analyticsCode0
Mediators: Conversational Agents Explaining NLP Model Behavior0
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