  Automated machine learning (AutoML) systems aim to enable training machine
learning (ML) models for non-ML experts. A shortcoming of these systems is that
when they fail to produce a model with high accuracy, the user has no path to
improve the model other than hiring a data scientist or learning ML -- this
defeats the purpose of AutoML and limits its adoption. We introduce an
interpretable data feedback solution for AutoML. Our solution suggests new data
points for the user to label (without requiring a pool of unlabeled data) to
improve the model's accuracy. Our solution analyzes how features influence the
prediction among all ML models in an AutoML ensemble, and we suggest more data
samples from feature ranges that have high variance in such analysis. Our
evaluation shows that our solution can improve the accuracy of AutoML by 7-8%
and significantly outperforms popular active learning solutions in data
efficiency, all the while providing the added benefit of being interpretable.
