Before revision: Explainable machine learning seeks to provide various stakeholders with insights into model behavior via feature importance scores, counterfactual explanations, and influential samples, among other techniques. Recent advances in this line of work, however, have gone without surveys of URLanizations are using these techniques in practice.
After revision: Explainable machine learning seeks to provide various stakeholders with insights into model behavior via feature importance scores, counterfactual explanations, or influential training data. Yet there is little understanding of URLanizations use these methods in practice.