  The growing capability and accessibility of machine learning has led to its
application to many real-world domains and data about people. Despite the
benefits algorithmic systems may bring, models can reflect, inject, or
exacerbate implicit and explicit societal biases into their outputs,
disadvantaging certain demographic subgroups. Discovering which biases a
machine learning model has introduced is a great challenge, due to the numerous
definitions of fairness and the large number of potentially impacted subgroups.
We present FairVis, a mixed-initiative visual analytics system that integrates
a novel subgroup discovery technique for users to audit the fairness of machine
learning models. Through FairVis, users can apply domain knowledge to generate
and investigate known subgroups, and explore suggested and similar subgroups.
FairVis' coordinated views enable users to explore a high-level overview of
subgroup performance and subsequently drill down into detailed investigation of
specific subgroups. We show how FairVis helps to discover biases in two real
datasets used in predicting income and recidivism. As a visual analytics system
devoted to discovering bias in machine learning, FairVis demonstrates how
interactive visualization may help data scientists and the general public
understand and create more equitable algorithmic systems.
