Towards fair machine learning using many-objective feature selection

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. • Introduction of a fair feature selection approach leveraging many-objective optimization. • Extensive experimentation and statistical validation demonstrating the effectiveness of our approach. • Comparison with three baseline methods, including IBM AI Fairness 360. • Identification of optimal termination criteria, specifically the number of generations, for effective convergence through hyperparameter tuning.