Revisiting the robustness of post-hoc interpretability methods
<|reference_start|>Revisiting the robustness of post-hoc interpretability methods: Post-hoc interpretability methods play a critical role in explainable artificial intelligence (XAI), as they pinpoint portions of data that a trained deep learning model deemed important to make a decision. However, different post-hoc interpretability methods often provide different results, casting doubts on their accuracy. For this reason, several evaluation strategies have been proposed to understand the accuracy of post-hoc interpretability. Many of these evaluation strategies provide a coarse-grained assessment -- i.e., they evaluate how the performance of the model degrades on average by corrupting different data points across multiple samples. While these strategies are effective in selecting the post-hoc interpretability method that is most reliable on average, they fail to provide a sample-level, also referred to as fine-grained, assessment. In other words, they do not measure the robustness of post-hoc interpretability methods. We propose an approach and two new metrics to provide a fine-grained assessment of post-hoc interpretability methods. We show that the robustness is generally linked to its coarse-grained performance.<|reference_end|>
@article{wei2024revisiting,
    title={Revisiting the robustness of post-hoc interpretability methods},
    author={Jiawen Wei, Hugues Turb'e, Gianmarco Mengaldo},
    journal={arXiv preprint arXiv:2407.19683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19683},
    primaryClass={cs.LG cs.AI}
}