# FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring

arXiv: https://arxiv.org/abs/2508.20021 · HTML: https://mlsanity.com/paper/2508.20021/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Fairness-Aware Process Mining](https://mlsanity.com/paper/1908.11451/) (2019)
- [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)
- [Recent Advances in Data-Driven Business Process Management](https://mlsanity.com/paper/2406.01786/) (2024)
- [Attention Please: What Transformer Models Really Learn for Process Prediction](https://mlsanity.com/paper/2408.07097/) (2024)
- [A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.17477/) (2025)

## Abstract (arXiv)

Sensitive attributes like gender or age can lead to unfair predictions in machine learning tasks such as predictive business process monitoring, particularly when used without considering context. We present FairLoop1, a tool for human-guided bias mitigation in neural network-based prediction models. FairLoop distills decision trees from neural networks, allowing users to inspect and modify unfair decision logic, which is then used to fine-tune the original model towards fairer predictions. Compared to other approaches to fairness, FairLoop enables context-aware bias removal through human involvement, addressing the influence of sensitive attributes selectively rather than excluding them uniformly.