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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 621630 of 971 papers

TitleStatusHype
Explainable Image Recognition via Enhanced Slot-attention Based Classifier0
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
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems0
Explainable Machine Learning for Predicting Homicide Clearance in the United States0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges0
Explainable Multi-Label Classification of MBTI Types0
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