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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 611620 of 971 papers

TitleStatusHype
A Theoretical Framework for AI Models Explainability with Application in Biomedicine0
GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network0
Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course DesignCode0
Interpretable ML for Imbalanced DataCode0
Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification0
XRand: Differentially Private Defense against Explanation-Guided Attacks0
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation0
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
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