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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

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Showing 426–450 of 971 papers

TitleStatusHype
Assessing high-order effects in feature importance via predictability decomposition—0
A multi-component framework for the analysis and design of explainable artificial intelligence—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review—0
Explainable Image Recognition via Enhanced Slot-attention Based Classifier—0
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey—0
Explainable Interface for Human-Autonomy Teaming: A Survey—0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification—0
Explainable Label-flipping Attacks on Human Emotion Assessment System—0
Concept-Based Explainable Artificial Intelligence: Metrics and Benchmarks—0
Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP—0
Concept-based Explainable Artificial Intelligence: A Survey—0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand—0
Explainable Machine Learning for Predicting Homicide Clearance in the United States—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Explainable Multi-Label Classification of MBTI Types—0
Explainable Multimodal Sentiment Analysis on Bengali Memes—0
Explainable Predictive Maintenance—0
Explainable Reinforcement Learning: A Survey—0
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey—0
Explainable Reinforcement Learning on Financial Stock Trading using SHAP—0
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines—0
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization—0
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?—0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis—0
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