The content delves into the comparison between counterfactual-guided and counterfactual-free methods for generating semi-factual explanations in eXplainable AI (XAI). It explores the necessity of using counterfactuals as guides and presents comprehensive tests on various metrics across different datasets. The results indicate that relying on counterfactuals does not always result in better semi-factual explanations. Instead, factors like distance, plausibility, confusability, robustness, and sparsity play crucial roles in determining the effectiveness of different methods. The study highlights the need for further research to combine the strengths of existing methods for optimal semi-factual generation.
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by Saugat Aryal... às arxiv.org 03-05-2024
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