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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 931940 of 971 papers

TitleStatusHype
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis0
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)0
Towards explainable meta-learning0
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model ConversationsCode0
Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective0
On the Explanation of Machine Learning Predictions in Clinical Gait AnalysisCode0
Improved Explanatory Efficacy on Human Affect and Workload through Interactive Process in Artificial Intelligence0
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches0
Explainable artificial intelligence model to predict acute critical illness from electronic health records0
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods0
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