Authors - Hatem Yousif Alkhonini, Fethi Fkih Abstract - This study evaluates five post hoc explanation methods using XAI-Bench under controlled settings with ground-truth explanations. Results show significant differences in robustness, with MAPLE outperforming Shapley-based methods under high correlation. Feature correlation impacts explanation quality more than the choice of method, and performance degrades with increasing dimensionality-especially for LIME. Exact methods become infeasible beyond d=10, and robust evaluation requires multi-seed replication.
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