Towards fair machine learning using many-objective feature selection
Uchechukwu F. Njoku, Alberto Abelló, Besim Bilalli, Gianluca Bontempi · Applied Soft Computing · 2025
This paper introduces Many-Objective Feature Selection (MOFS) as a novel approach for enhancing fairness in machine learning models. MOFS simultaneously optimizes model complexity, performance, and fairness using evolutionary algorithms to ensure that the selected features lead to a balanced and equitable model. We evaluate MOFS across 15 diverse fairness datasets, comparing it to three baseline methods, including IBM AI Fairness 360. Statistical analysis of our results demonstrates that MOFS consistently and significantly achieves higher accuracy and improves fairness metrics such as demographic parity and equalized odds. The study further examines the robustness and scalability of the MOFS approach across the datasets. The consistently high hypervolume metric indicator confirms that a balance between model complexity, performance, and fairness is achieved. This work highlights the potential of MOFS to develop fairer machine learning models while reducing model complexity and without compromising performance.