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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 211220 of 971 papers

TitleStatusHype
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications0
Approximating the Shapley Value without Marginal Contributions0
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps0
Causal Explanations and XAI0
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss0
Adapting the Biological SSVEP Response to Artificial Neural Networks0
Data integration in systems genetics and aging research0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods0
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis0
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