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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 201210 of 971 papers

TitleStatusHype
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions0
XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models0
An Explainable Fast Deep Neural Network for Emotion Recognition0
End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligenceCode0
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment0
Are Linear Regression Models White Box and Interpretable?0
XEQ Scale for Evaluating XAI Experience Quality0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review0
Robustness of Explainable Artificial Intelligence in Industrial Process Modelling0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging0
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