Poster
Framework for Evaluating Faithfulness of Local Explanations
Sanjoy Dasgupta · Nave Frost · Michal Moshkovitz
Hall E #921
Keywords: [ T: Learning Theory ] [ T: Social Aspects ] [ SA: Accountability, Transparency and Interpretability ]
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.