Fairness-Aware Process Mining

TL;DR

This paper introduces a fairness-aware classifier for process mining that reduces discrimination in diagnostic results, implemented as a ProM plug-in, balancing fairness and accuracy on real event logs.

Contribution

It presents a novel fairness-aware classification method for process mining, addressing bias and discrimination issues in root cause analysis.

Findings

Reduced discrimination in classifiers on real logs

Minor loss in classification accuracy

Effective implementation as a ProM plug-in

Abstract

Process mining is a multi-purpose tool enabling organizations to improve their processes. One of the primary purposes of process mining is finding the root causes of performance or compliance problems in processes. The usual way of doing so is by gathering data from the process event log and other sources and then applying some data mining and machine learning techniques. However, the results of applying such techniques are not always acceptable. In many situations, this approach is prone to making obvious or unfair diagnoses and applying them may result in conclusions that are unsurprising or even discriminating (e.g., blaming overloaded employees for delays). In this paper, we present a solution to this problem by creating a fair classifier for such situations. The undesired effects are removed at the expense of reduction on the accuracy of the resulting classifier. We have implemented this method as a plug-in in ProM. Using the implemented plug-in on two real event logs, we decreased the discrimination caused by the classifier, while losing a small fraction of its accuracy.