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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 321330 of 971 papers

TitleStatusHype
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning0
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning0
Automated Explanation Selection for Scientific Discovery0
Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence0
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data0
Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence0
Détection d'objets célestes dans des images astronomiques par IA explicable0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models0
A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)0
Adversarial Attack for Explanation Robustness of Rationalization Models0
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