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. 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.
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Notes
- 1.
We assume the reader to be familiar with the concepts like set, multi-set, and function. Given a non-empty set X, we denote all the non-empty subsets of X by \(\mathbb {P}(X)\). Given two sets A and B, a partial function
is defined as a function \(f:A'\mapsto B\) for some \(A'\subseteq A\). We say \(f(a) = \bot \) if \(a \not \in A'\). - 2.
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Qafari, M.S., van der Aalst, W. (2019). Fairness-Aware Process Mining. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C., Meersman, R. (eds) On the Move to Meaningful Internet Systems: OTM 2019 Conferences. OTM 2019. Lecture Notes in Computer Science(), vol 11877. Springer, Cham. https://doi.org/10.1007/978-3-030-33246-4_11
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