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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 151160 of 971 papers

TitleStatusHype
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
Cartan moving frames and the data manifoldsCode0
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognitionCode0
Ensemble of Counterfactual ExplainersCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
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