  Existing work on fairness typically focuses on making known machine learning
algorithms fairer. Fair variants of classification, clustering, outlier
detection and other styles of algorithms exist. However, an understudied area
is the topic of auditing an algorithm's output to determine fairness. Existing
work has explored the two group classification problem for binary protected
status variables using standard definitions of statistical parity. Here we
build upon the area of auditing by exploring the multi-group setting under more
complex definitions of fairness.
