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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 511520 of 971 papers

TitleStatusHype
XAI-KG: knowledge graph to support XAI and decision-making in manufacturing0
XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models0
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts0
XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs0
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization0
X-DFS: Explainable Artificial Intelligence Guided Design-for-Security Solution Space Exploration0
XEQ Scale for Evaluating XAI Experience Quality0
XRand: Differentially Private Defense against Explanation-Guided Attacks0
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life0
Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted?0
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