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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 571580 of 971 papers

TitleStatusHype
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning0
The Disagreement Problem in Faithfulness Metrics0
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations0
The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?0
The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI0
The Literature Review Network: An Explainable Artificial Intelligence for Systematic Literature Reviews, Meta-analyses, and Method Development0
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables0
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)0
The role of causality in explainable artificial intelligence0
The Role of Deep Learning in Financial Asset Management: A Systematic Review0
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