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Existing work on fairness typically focuses on making known machine learningalgorithms fairer. Fair variants of classification, clustering, outlierdetection and other styles of algorithms exist. However, an understudied areais the topic of auditing an algorithm's output to determine fairness. Existingwork has explored the two group classification problem for binary protectedstatus variables using standard definitions of statistical parity. Here webuild upon the area of auditing by exploring the multi-group setting under morecomplex definitions of fairness.

Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness