Framework for Evaluating Faithfulness of Local Explanations

@InProceedings{pmlr-v162-dasgupta22a,
  title = 	 {Framework for Evaluating Faithfulness of Local Explanations},
  author =       {Dasgupta, Sanjoy and Frost, Nave and Moshkovitz, Michal},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {4794--4815},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/dasgupta22a/dasgupta22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/dasgupta22a.html},
  abstract = 	 {We study the faithfulness of an explanation system to the underlying prediction model. We show that this can be captured by two properties, consistency and sufficiency, and introduce quantitative measures of the extent to which these hold. Interestingly, these measures depend on the test-time data distribution. For a variety of existing explanation systems, such as anchors, we analytically study these quantities. We also provide estimators and sample complexity bounds for empirically determining the faithfulness of black-box explanation systems. Finally, we experimentally validate the new properties and estimators.}
}