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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 181190 of 971 papers

TitleStatusHype
Dataset | Mindset = Explainable AI | Interpretable AI0
Explainable Anomaly Detection: Counterfactual driven What-If Analysis0
Explainable Deep Learning Framework for Human Activity Recognition0
Adversarial Attack for Explanation Robustness of Rationalization Models0
Measuring User Understanding in Dialogue-based XAI Systems0
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing0
Enhanced Prototypical Part Network (EPPNet) For Explainable Image Classification Via Prototypes0
SCENE: Evaluating Explainable AI Techniques Using Soft Counterfactuals0
The Literature Review Network: An Explainable Artificial Intelligence for Systematic Literature Reviews, Meta-analyses, and Method Development0
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