  As machine learning systems become democratized, it becomes increasingly
important to help users easily debug their models. However, current data tools
are still primitive when it comes to helping users trace model performance
problems all the way to the data. We focus on the particular problem of slicing
data to identify subsets of the validation data where the model performs
poorly. This is an important problem in model validation because the overall
model performance can fail to reflect that of the smaller subsets, and slicing
allows users to analyze the model performance on a more granular-level. Unlike
general techniques (e.g., clustering) that can find arbitrary slices, our goal
is to find interpretable slices (which are easier to take action compared to
arbitrary subsets) that are problematic and large. We propose Slice Finder,
which is an interactive framework for identifying such slices using statistical
techniques. Applications include diagnosing model fairness and fraud detection,
where identifying slices that are interpretable to humans is crucial. This
research is part of a larger trend of Big data and Artificial Intelligence (AI)
integration and opens many opportunities for new research.
