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

In three linesarXiv paper proposing counterfactual explainability grounded in uncertainty quantification. Authors demonstrate that integrating foundational AI concepts—particularly uncertainty—improves robustness and reliability of explanations, achieving competitive performance despite radically simple design.

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