  As machine learning (ML) systems become increasingly widespread, it is
necessary to audit these systems for biases prior to their deployment. Recent
research has developed algorithms for effectively identifying intersectional
bias in the form of interpretable, underperforming subsets (or slices) of the
data. However, these solutions and their insights are limited without a tool
for visually understanding and interacting with the results of these
algorithms. We propose Visual Auditor, an interactive visualization tool for
auditing and summarizing model biases. Visual Auditor assists model validation
by providing an interpretable overview of intersectional bias (bias that is
present when examining populations defined by multiple features), details about
relationships between problematic data slices, and a comparison between
underperforming and overperforming data slices in a model. Our open-source tool
runs directly in both computational notebooks and web browsers, making model
auditing accessible and easily integrated into current ML development
workflows. An observational user study in collaboration with domain experts at
Fiddler AI highlights that our tool can help ML practitioners identify and
understand model biases.
