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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 571580 of 971 papers

TitleStatusHype
Causality-Aware Local Interpretable Model-Agnostic ExplanationsCode1
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation0
XRand: Differentially Private Defense against Explanation-Guided Attacks0
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
Foiling Explanations in Deep Neural NetworksCode0
Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research0
MEGAN: Multi-Explanation Graph Attention NetworkCode1
Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images0
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations0
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