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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 371380 of 971 papers

TitleStatusHype
A survey on Concept-based Approaches For Model Improvement0
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey0
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making0
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving0
Towards a general framework for improving the performance of classifiers using XAI methods0
Explainability through uncertainty: Trustworthy decision-making with neural networks0
Interpretable Machine Learning for Survival AnalysisCode0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems0
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization0
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