FairRF

This repository contains the code for the paper "FairRF: Multi-Objective Search for Single and Intersectional Software Fairness", accepted for publication at ICSE-SEIS 2026.

Repository Structure

• analysis: Contains the raw data and the jupyter notebooks used for the analysis and the generation of the plots.
• baseline: Contains the code to run the base classification models (RQ2).
• baseline_intersectional: Contains the code to run the FairHOME approach for intersectional fairness (RQ4).
• bias_mitigation_methods: Contains the results of the state-of-the-art bias mitigation methods used for comparison (RQ3).
• fair_rf: Contains the implementation of FairRF and its variants (RQ1).
• fair_rf_multi_attribute: Contains the implementation of FairRF for intersectional fairness (RQ4).
• random_search: Contains the implementation of the random search algorithm used as a baseline for comparison (RQ2).

In all folders, we provide a run_all.sh script to reproduce the experiments.

Installation

Install Python 3.9 or higher.

Conda

To create the conda environment, run:

conda env create -f environment.yml
conda activate mutrf

Pip

Alternatively, you can install the required packages using pip:

pip install -r requirements.txt

Datasets

Follow the instructions reported here https://github.com/Trusted-AI/AIF360/tree/7c4f172f4a81b1d14d19400e95c468aaf3134952/aif360/data to download the datasets used in the paper.