Discovering the systematic errors made by machine learning models

In this blog post, we introduce Domino, a new approach for discovering systematic errors made by machine learning models. We also discuss a framework for quantitatively evaluating methods like Domino. Machine learning models that achieve high overall accuracy often make systematic errors on coherent slices of validation data. A slice is a set of data samples that share a common characteristic. As an example, in large image datasets, photos of vintage cars comprise a slice (i.e.