xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
The growing complexity of machine learning and deep learning models has led
to an increased reliance on opaque "black box" systems, making it difficult to
understand the rationale behind predictions. This lack of transparency is
particularly challenging in high-stakes applications where interpretability is
as important as accuracy. Post-hoc explanation methods are commonly used to
interpret these models, but they are seldom rigorously evaluated, raising
concerns about their reliability. The Python package xai_evals addresses this
by providing a comprehensive framework for generating, benchmarking, and
evaluating explanation methods across both tabular and image data modalities.
It integrates popular techniques like SHAP, LIME, Grad-CAM, Integrated
Gradients (IG), and Backtrace, while supporting evaluation metrics such as
faithfulness, sensitivity, and robustness. xai_evals enhances the
interpretability of machine learning models, fostering transparency and trust
in AI systems. The library is open-sourced at
https://pypi.org/project/xai-evals/ .
