# Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)

arXiv: https://arxiv.org/abs/2410.02618 · HTML: https://mlsanity.com/paper/2410.02618/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Auditing Black-box Models for Indirect Influence](https://mlsanity.com/paper/1602.07043/) (2016)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Cohort Shapley value for algorithmic fairness](https://mlsanity.com/paper/2105.07168/) (2021)
- [Fairness-Aware Process Mining](https://mlsanity.com/paper/1908.11451/) (2019)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [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)

- [Achieving Group Fairness through Independence in Predictive Process Monitoring](https://mlsanity.com/paper/2412.04914/) (2024)
- [A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.17477/) (2025)
- [Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring](https://mlsanity.com/paper/2507.18293/) (2025)
- [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 business process analytics has become important for organizations, offering real-time operational support for their processes. However, these algorithms often perform unfair predictions because they are based on biased variables (e.g., gender or nationality), namely variables embodying discrimination. This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics to ensure that predictions are not influenced by biased variables. Our framework leverages on adversial debiasing is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value. The proposed technique is also compared with the state of the art in fairness in process mining, illustrating that our framework allows for a more enhanced level of fairness, while retaining a better prediction quality.