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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 651660 of 971 papers

TitleStatusHype
Position: Explain to Question not to Justify0
Explain yourself! Effects of Explanations in Human-Robot Interaction0
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing0
Explanation in Artificial Intelligence: Insights from the Social Sciences0
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI0
Explanation User Interfaces: A Systematic Literature Review0
Exploiting Explanations for Model Inversion Attacks0
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises0
Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond0
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