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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 151160 of 971 papers

TitleStatusHype
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques0
Argumentation Theoretical Frameworks for Explainable Artificial Intelligence0
A Meta Survey of Quality Evaluation Criteria in Explanation Methods0
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer0
Argumentation-based Agents that Explain their Decisions0
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?0
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making0
A Means-End Account of Explainable Artificial Intelligence0
A Review of Explainable Artificial Intelligence in Manufacturing0
A Data-Driven Framework for Identifying Investment Opportunities in Private Equity0
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