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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 431440 of 971 papers

TitleStatusHype
Explainable Incipient Fault Detection Systems for Photovoltaic Panels0
Explainable Interface for Human-Autonomy Teaming: A Survey0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification0
Explainable Label-flipping Attacks on Human Emotion Assessment System0
A general approach to compute the relevance of middle-level input features0
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments0
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models0
Data integration in systems genetics and aging research0
Explainable Machine Learning for Predicting Homicide Clearance in the United States0
Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation0
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