FairRF: Multi-Objective Search for Single and Intersectional Software Fairness

2026-01-12

AI Summary

This work addresses the challenge of simultaneously ensuring predictive effectiveness and fairness in AI/ML systems deployed in sensitive domains, where black-box models often fail to adequately handle both unidimensional and intersectional biases. The authors propose a multi-objective evolutionary search framework that integrates random forests with data perturbation techniques to jointly optimize five performance metrics and six fairness criteria—including intersectional fairness—for the first time enabling concurrent improvement of both unidimensional and intersectional fairness. The approach yields a customizable Pareto frontier of fairness-effectiveness trade-offs, allowing stakeholders to select solutions aligned with their priorities. Evaluated across 11 real-world scenarios, the method significantly outperforms 26 baseline approaches, maintaining high predictive accuracy while achieving state-of-the-art performance in mitigating intersectional bias.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Background: The wide adoption of AI- and ML-based systems in sensitive domains raises severe concerns about their fairness. Many methods have been proposed in the literature to enhance software fairness. However, the majority behave as a black-box, not allowing stakeholders to prioritise fairness or effectiveness (i.e., prediction correctness) based on their needs. Aims: In this paper, we introduce FairRF, a novel approach based on multi-objective evolutionary search to optimise fairness and effectiveness in classification tasks. FairRF uses a Random Forest (RF) model as a base classifier and searches for the best hyperparameter configurations and data mutation to maximise fairness and effectiveness. Eventually, it returns a set of Pareto optimal solutions, allowing the final stakeholders to choose the best one based on their needs. Method: We conduct an extensive empirical evaluation of FairRF against 26 different baselines in 11 different scenarios using five effectiveness and three fairness metrics. Additionally, we also include two variations of the fairness metrics for intersectional bias for a total of six definitions analysed. Result: Our results show that FairRF can significantly improve the fairness of base classifiers, while maintaining consistent prediction effectiveness. Additionally, FairRF provides a more consistent optimisation under all fairness definitions compared to state-of-the-art bias mitigation methods and overcomes the existing state-of-the-art approach for intersectional bias mitigation. Conclusions: FairRF is an effective approach for bias mitigation also allowing stakeholders to adapt the development of fair software systems based on their specific needs.

Problem

• software fairness
• intersectional bias
• fairness-effectiveness trade-off
• AI fairness
• bias mitigation

Innovation

• multi-objective optimization
• fairness-aware machine learning
• Random Forest
• intersectional bias
• Pareto optimal solutions