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.
|Number of pages||22|
|Journal||Proceedings of Machine Learning Research|
|State||Published - 2022|
|Event||39th International Conference on Machine Learning, ICML 2022 - Baltimore, United States|
Duration: 17 Jul 2022 → 23 Jul 2022