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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 501–525 of 971 papers

TitleStatusHype
When concept-based XAI is imprecise: Do people distinguish between generalisations and misrepresentations?—0
Where and When: Space-Time Attention for Audio-Visual Explanations—0
Which LIME should I trust? Concepts, Challenges, and Solutions—0
Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal—0
Widespread Increases in Future Wildfire Risk to Global Forest Carbon Offset Projects Revealed by Explainable AI—0
XAI and Android Malware Models—0
XAI Benchmark for Visual Explanation—0
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics—0
XAI for All: Can Large Language Models Simplify Explainable AI?—0
XAI-FUNGI: Dataset resulting from the user study on comprehensibility of explainable AI algorithms—0
XAI-KG: knowledge graph to support XAI and decision-making in manufacturing—0
XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models—0
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts—0
XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs—0
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization—0
X-DFS: Explainable Artificial Intelligence Guided Design-for-Security Solution Space Exploration—0
XEQ Scale for Evaluating XAI Experience Quality—0
XRand: Differentially Private Defense against Explanation-Guided Attacks—0
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life—0
Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted?—0
Evolutionary approaches to explainable machine learning—0
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems—0
EVolutionary Independent DEtermiNistiC Explanation—0
Evolved Explainable Classifications for Lymph Node Metastases—0
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks—0
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