מושגי ליבה
Counterfactuals and counterfactual-free methods are compared to determine the best semi-factual explanations in XAI, revealing that the use of counterfactual guidance does not necessarily lead to superior results.
תקציר
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.
סטטיסטיקה
MDN method scores: Distance - 0.255, Plausibility - 0.046, Confusability - 0.996, Robustness - 0.53, Sparsity - 0.
Local-Region method scores: Distance - 0.364, Plausibility - 0.069, Confusability - 0.967, Robustness - 0.967, Sparsity - 0.19.
DSER method scores: Distance - 0.024, Plausibility - 0.062, Confusability - 1, Robustness - 0.99, Sparsity - 1.
S-GEN method scores: Distance - 0.518, Plausibility - 0.126, Confusability - 0.9, Robustness - 0.991, Sparsity – 0.33.
C2C VAE method scores: Distance – 0.488 , Plausibility – 0 .078 , Confusability – 1 , Robustness – 1 , Sparsity – 1 .
ציטוטים
"Even if explanations provide diverse semifactual insights."
"Counterfactual guidance is not a major factor in finding the best semi-factuals."
"Semi-factual methods show varying performance across different evaluation metrics."