# Achieving Group Fairness through Independence in Predictive Process Monitoring

arXiv: https://arxiv.org/abs/2412.04914 · HTML: https://mlsanity.com/paper/2412.04914/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved](https://mlsanity.com/paper/1811.11154/) (2018)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Clustering-Based Predictive Process Monitoring](https://mlsanity.com/paper/1506.01428/) (2015)
- [Learning fair representation with a parametric integral probability metric](https://mlsanity.com/paper/2202.02943/) (2022)
- [Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring](https://mlsanity.com/paper/2107.01905/) (2021)
- [Fair Bayes-Optimal Classifiers Under Predictive Parity](https://mlsanity.com/paper/2205.07182/) (2022)
- [Fairness-Aware Process Mining](https://mlsanity.com/paper/1908.11451/) (2019)
- [Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)](https://mlsanity.com/paper/2410.02618/) (2024)
- [Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities](https://mlsanity.com/paper/2110.02707/) (2021)
- [A Collection of Simulated Event Logs for Fairness Assessment in Process Mining](https://mlsanity.com/paper/2306.11453/) (2023)

## Built on by (most cited first, 5 of 5)

- [Decision-centric fairness: Evaluation and optimization for resource allocation problems](https://mlsanity.com/paper/2504.20642/) (2025)
- [A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.17477/) (2025)
- [Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach](https://mlsanity.com/paper/2603.26948/) (2026)
- [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)

## Abstract (arXiv)

Predictive process monitoring focuses on forecasting future states of ongoing process executions, such as predicting the outcome of a particular case. In recent years, the application of machine learning models in this domain has garnered significant scientific attention. When using historical execution data, which may contain biases or exhibit unfair behavior, these biases may be encoded into the trained models. Consequently, when such models are deployed to make decisions or guide interventions for new cases, they risk perpetuating this unwanted behavior. This work addresses group fairness in predictive process monitoring by investigating independence, i.e. ensuring predictions are unaffected by sensitive group membership. We explore independence through metrics for demographic parity such as DeltaDP, as well as recently introduced, threshold-independent distribution-based alternatives. Additionally, we propose a composite loss function existing of binary cross-entropy and a distribution-based loss (Wasserstein) to train models that balance predictive performance and fairness, and allow for customizable trade-offs. The effectiveness of both the fairness metrics and the composite loss functions is validated through a controlled experimental setup.