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.