The Generalizability of Explanations

Due to the absence of ground truth, objective evaluation of explainability methods is an essential research direction. So far, the vast majority of evaluations can be summarized into three categories, namely human evaluation, sensitivity testing, and salinity check. This work proposes a novel evaluation methodology from the perspective of generalizability. We employ an Autoencoder to learn the distributions of the generated explanations and observe their learnability as well as the plausibility of the learned distributional features. We first briefly demonstrate the evaluation idea of the proposed approach at LIME, and then quantitatively evaluate multiple popular explainability methods. We also find that smoothing the explanations with SmoothGrad can significantly enhance the generalizability of explanations.

  • Published in:
    arXiv
  • Type:
    Article
  • Authors:
    Tan, Hanxiao
  • Year:
    2023

Citation information

Tan, Hanxiao: The Generalizability of Explanations, arXiv, 2023, https://arxiv.org/abs/2302.11965, Tan.2023b,

Associated Lamarr Researchers

lamarr institute person hanxiao tan - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Hanxiao Tan

Scientist to the profile