Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations

Fuente: arXiv

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Bibliographic Details
Main Authors:	Sokol, Kacper, Thies, Santo M. A. R., Hüllermeier, Eyke
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2502.17007