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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 711720 of 971 papers

TitleStatusHype
Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models0
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications0
Human in the AI loop via xAI and Active Learning for Visual Inspection0
Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach0
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data0
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence0
ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural Faithfulness in SpArX0
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study0
Impact of Feature Encoding on Malware Classification Explainability0
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution0
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