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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 291300 of 971 papers

TitleStatusHype
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection0
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis0
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors0
The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI0
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey0
A survey on Concept-based Approaches For Model Improvement0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making0
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks0
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving0
Interpretable Machine Learning for Survival AnalysisCode0
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