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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 581590 of 971 papers

TitleStatusHype
The Role of XAI in Transforming Aeronautics and Aerospace Systems0
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research0
TimeREISE: Time-series Randomized Evolving Input Sample Explanation0
Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactions0
Towards a general framework for improving the performance of classifiers using XAI methods0
Towards a Grounded Dialog Model for Explainable Artificial Intelligence0
Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-being0
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series0
Towards a Rigorous Evaluation of XAI Methods on Time Series0
Towards a Shapley Value Graph Framework for Medical peer-influence0
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