xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods
To address the lack of rigorous evaluation criteria for post-hoc explanation methods for black-box models, this paper introduces the first open-source, multimodal evaluation framework supporting both tabular and image data. The framework unifies three core dimensions—faithfulness, sensitivity, and robustness—into a reproducible, systematic benchmarking protocol. It integrates major explanation methods including SHAP, LIME, Grad-CAM, Integrated Gradients, and Backpropagation-based Trace (Backtrace), and supports models implemented in PyTorch and TensorFlow, as well as user-defined explainers. Extensive experiments on UCI tabular benchmarks and ImageNet demonstrate substantial performance variation across methods, underscoring the necessity of standardized evaluation. Our framework significantly improves the efficiency, comparability, and reproducibility of explanation credibility assessment. The implementation is publicly released and has been widely adopted by the research community.