# Fairness-Aware Process Mining

arXiv: https://arxiv.org/abs/1908.11451 · HTML: https://mlsanity.com/paper/1908.11451/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)](https://mlsanity.com/paper/2410.02618/) (2024)
- [Achieving Group Fairness through Independence in Predictive Process Monitoring](https://mlsanity.com/paper/2412.04914/) (2024)
- [Large Process Models: A Vision for Business Process Management in the Age of Generative AI](https://mlsanity.com/paper/2309.00900/) (2023)
- [A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.17477/) (2025)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)
- [FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.20021/) (2025)
- [Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triage](https://mlsanity.com/paper/2601.11065/) (2026)
- [LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines](https://mlsanity.com/paper/2606.09489/) (2026)

## Abstract (arXiv)

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.