Impossibility theorems for feature attribution
成果类型：
Article
署名作者：
Bilodeau, Blair; Jaques, Natasha; Koh, Pang Wei; Kim, Been
署名单位：
University of Toronto; University of Washington; University of Washington Seattle; Alphabet Inc.; DeepMind
刊物名称：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
ISSN/ISSBN：
0027-12642
DOI：
10.1073/pnas.2304406120
发表日期：
2024-01-09
关键词：
摘要：
Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods. In light of these results, it remains unclear for practitioners how to use these methods and choose between them in a principled way. In this paper, we show that for moderately rich model classes (easily satisfied by neural networks), any feature attribution method that is complete and linear-for example, Integrated Gradients and Shapley Additive Explanations (SHAP)-can provably fail to improve on random guessing for inferring model behavior. Our results apply to common end-tasks such as characterizing local model behavior, identifying spurious features, and algorithmic recourse. One takeaway from our work is the importance of concretely defining end-tasks: Once such an end-task is defined, a simple and direct approach of repeated model evaluations can outperform many other complex feature attribution methods.