A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoring

TL;DR

This paper introduces a human-in-the-loop method to identify and correct biased decisions in predictive business process models, balancing fairness and accuracy even when sensitive attributes are used variably.

Contribution

It presents a novel, model-agnostic approach that differentiates fair and unfair decisions using decision tree models, improving fairness without sacrificing predictive performance.

Findings

Achieves a promising fairness-accuracy tradeoff

Effectively identifies biased decisions in process models

Source code and data are publicly available

Abstract

Predictive process monitoring enables organizations to proactively react and intervene in running instances of a business process. Given an incomplete process instance, predictions about the outcome, next activity, or remaining time are created. This is done by powerful machine learning models, which have shown impressive predictive performance. However, the data-driven nature of these models makes them susceptible to finding unfair, biased, or unethical patterns in the data. Such patterns lead to biased predictions based on so-called sensitive attributes, such as the gender or age of process participants. Previous work has identified this problem and offered solutions that mitigate biases by removing sensitive attributes entirely from the process instance. However, sensitive attributes can be used both fairly and unfairly in the same process instance. For example, during a medical process, treatment decisions could be based on gender, while the decision to accept a patient should not be based on gender. This paper proposes a novel, model-agnostic approach for identifying and rectifying biased decisions in predictive business process monitoring models, even when the same sensitive attribute is used both fairly and unfairly. The proposed approach uses a human-in-the-loop approach to differentiate between fair and unfair decisions through simple alterations on a decision tree model distilled from the original prediction model. Our results show that the proposed approach achieves a promising tradeoff between fairness and accuracy in the presence of biased data. All source code and data are publicly available at https://doi.org/10.5281/zenodo.15387576.