Automated Data Slicing for Model Validation: A Big data - AI Integration Approach
Abstract
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
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