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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 841850 of 971 papers

TitleStatusHype
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series0
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing0
STARdom: an architecture for trusted and secure human-centered manufacturing systems0
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey0
Local Explanations via Necessity and Sufficiency: Unifying Theory and PracticeCode0
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals0
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI0
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications0
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence0
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery0
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