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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 271280 of 971 papers

TitleStatusHype
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black BoxCode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
Eliminating The Impossible, Whatever Remains Must Be TrueCode0
An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray ImagesCode0
Does Dataset Complexity Matters for Model Explainers?Code0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI EvaluationCode0
Ensemble of Counterfactual ExplainersCode0
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