Multi-Objective Optimization in Machine Learning for Balancing Accuracy, Fairness and Efficiency
TL;DR
A multi-objective optimization framework that simultaneously integrates accuracy, fairness, and efficiency within the model development process using Pareto-based optimization techniques is proposed, enabling the development of models that are not only accurate but also fair and efficient.
Abstract
Machine learning models are traditionally optimized for predictive accuracy, often overlooking critical aspects such as fairness and computational efficiency, which are essential for real-world deployment in socially sensitive and resource-constrained environments. This creates a significant research gap, as existing approaches typically address fairness or efficiency in isolation, lacking a unified framework that systematically balances multiple objectives. To address this limitation, this study proposes a multi-objective optimization framework that simultaneously integrates accuracy, fairness, and efficiency within the model development process using Pareto-based optimization techniques. The methodology involves training multiple machine learning models across benchmark datasets containing sensitive attributes, enabling the evaluation of trade-offs between objectives. The framework employs fairness metrics such as demographic parity and equal opportunity, alongside computational efficiency indicators including training time and resource utilization. Pareto front analysis is used to identify optimal model configurations that achieve balanced performance across competing criteria. The results demonstrate that the proposed approach achieves accuracy levels within 1–3% of the best-performing single-objective models, while reducing fairness disparities by up to 40% and computational cost by approximately 20–30%. Statistical analysis confirms that improvements in fairness and efficiency are significant (p < 0.01), with no statistically significant loss in accuracy. These findings highlight the effectiveness of multi-objective optimization in producing balanced and deployable machine learning systems. This study aims to advance a holistic optimization paradigm for responsible AI, enabling the development of models that are not only accurate but also fair and efficient, thereby aligning machine learning practices with ethical and operational requirements