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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 91100 of 971 papers

TitleStatusHype
An Explainable AI Framework for Artificial Intelligence of Medical Things0
An Experimentation Platform for Explainable Coalition Situational Understanding0
Adversarial Attack for Explanation Robustness of Rationalization Models0
A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)0
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence0
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications0
A New Deep Learning and XAI-Based Algorithm for Features Selection in Genomics0
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions0
An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model0
Adherence and Constancy in LIME-RS Explanations for Recommendation0
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