AINeutralarXiv – CS AI · Mar 264/10
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No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertainty Attributions
Researchers propose a new framework for evaluating uncertainty attribution methods in explainable AI, addressing inconsistent evaluation practices in the field. The study introduces five key properties including a new 'conveyance' metric and demonstrates that gradient-based methods outperform perturbation-based approaches across multiple evaluation criteria.