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

## TL;DR

FairLoop is a human-in-the-loop tool that helps identify and mitigate bias in neural network predictions for business processes by allowing user inspection and modification of decision logic to promote fairness.

## Contribution

It introduces a novel approach combining decision tree extraction from neural networks with human-guided bias mitigation for fairer predictive models.

## Key findings

- Enables inspection and editing of decision logic for fairness
- Improves model fairness through human-guided bias mitigation
- Addresses context-specific bias in predictive models

## Abstract

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.