Multi-Objective Optimization in Machine Learning for Balancing Accuracy, Fairness and Efficiency
DOI:
https://doi.org/10.61453/jods.v20260209Keywords:
Multi-Objective Optimization, Fair Machine Learning, Model Efficiency, Responsible AI, Data ScienceAbstract
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.
References
Arora, A., Alderman, J. E., Palmer, J., Ganapathi, S., Laws, E., McCradden, M. D., Oakden-Rayner, L., Pfohl, S. R., Ghassemi, M., McKay, F., Treanor, D., Rostamzadeh, N., Mateen, B., Gath, J., Adebajo, A. O., Kuku, S., Matin, R., Heller, K., Sapey, E., … Liu, X. (2023). The value of standards for health datasets in artificial intelligence-based applications. Nature Medicine 2023 29:11, 29(11), 2929–2938. https://doi.org/10.1038/s41591-023-02608-w
Bahrami, P., Sahari Moghaddam, F., & James, L. A. (2022). A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering. Energies 2022, Vol. 15, 15(14). https://doi.org/10.3390/EN15145247
Blank, J., & Deb, K. (2022). Handling constrained multi-objective optimization problems with heterogeneous evaluation times: proof-of-principle results. Memetic Computing 2022 14:2, 14(2), 135–150. https://doi.org/10.1007/S12293-022-00362-Z
Breure, T. S., Estrada-Carmona, N., Petsakos, A., Gotor, E., Jansen, B., & Groot, J. C. J. (2024). A systematic review of the methodology of trade-off analysis in agriculture. Nature Food 2024 5:3, 5(3), 211–220. https://doi.org/10.1038/s43016-024-00926-x
Chen, T., & Li, M. (2023). Do Performance Aspirations Matter for Guiding Software Configuration Tuning? An Empirical Investigation under Dual Performance Objectives. ACM Transactions on Software Engineering and Methodology, 32(3), p.1-41. https://doi.org/10.1145/3571853
Chen, Y. F., Chan, W. H., Su, E. L. M., & Diao, Q. (2025). Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions. PeerJ Computer Science, 11, e3042. https://doi.org/10.7717/PEERJ-CS.3042/FIG-9
Deho, O. B., Joksimovic, S., Li, J., Zhan, C., Liu, J., & Liu, L. (2023). Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why. IEEE Transactions on Learning Technologies, 16(4), 560–572. https://doi.org/10.1109/TLT.2022.3226474
Feng, D., Li, Y., Liu, J., & Liu, Y. (2024). A particle swarm optimization algorithm based on modified crowding distance for multimodal multi-objective problems. Applied Soft Computing, 152, 111280. https://doi.org/10.1016/J.ASOC.2024.111280
Hanna, M. G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., Deebajah, M., & Rashidi, H. H. (2025). Ethical and Bias Considerations in Artificial Intelligence/Machine Learning. Modern Pathology, 38(3), 100686. https://doi.org/10.1016/J.MODPAT.2024.100686
Kumar, S., Sharma, D., Rao, S., Lim, W. M., & Mangla, S. K. (2022). Past, present, and future of sustainable finance: insights from big data analytics through machine learning of scholarly research. Annals of Operations Research 2021 345:2, 345(2), 1061–1104. https://doi.org/10.1007/S10479-021-04410-8
Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., & Ntoutsi, E. (2022). A survey on datasets for fairness-aware machine learning. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(3), e1452. https://doi.org/10.1002/widm.1452
Levinson, M., Geron, T., & Brighouse, H. (2022). Conceptions of Educational Equity. AERA Open, 8. https://doi.org/10.1177/23328584221121344
Muralidhar, R., Borovica-Gajic, R., & Buyya, R. (2022). Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards. ACM Computing Surveys (CSUR), 54(11s). https://doi.org/10.1145/3511094
N., H. K., & Padala, S. P. S. (2025). A BIM-integrated multi objective optimization model for sustainable building construction management. Construction Innovation, 25(6), 1727–1751. https://doi.org/10.1108/CI-09-2023-0223
Pagano, T. P., Loureiro, R. B., Lisboa, F. V. N., Peixoto, R. M., Guimarães, G. A. S., Cruz, G. O. R., Araujo, M. M., Santos, L. L., Cruz, M. A. S., Oliveira, E. L. S., Winkler, I., & Nascimento, E. G. S. (2023). Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods. Big Data and Cognitive Computing 2023, Vol. 7, 7(1). https://doi.org/10.3390/BDCC7010015
Pfob, A., Lu, S. C., & Sidey-Gibbons, C. (2022). Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Medical Research Methodology 2022 22:1, 22(1), 282-. https://doi.org/10.1186/S12874-022-01758-8
Price-Broncucia, T., Baker, A., Hammerling, D., Duda, M., & Morrison, R. (2025). The ensemble consistency test: from CESM to MPAS and beyond. Geoscientific Model Development, 18(8), 2349–2372. https://doi.org/10.5194/GMD-18-2349-2025
Rahaman, M. A., & Mohamad Idris, R. (2026). A stacking ensemble with Pareto optimization for scalable electricity theft detection via hybrid data repair and lightweight deployment. Scientific Reports 2026. https://doi.org/10.1038/s41598-026-39693-z
Rajput, S., Widmayer, T., Shang, Z., Kechagia, M., Sarro, F., & Sharma, T. (2024). Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement. ACM Transactions on Software Engineering and Methodology, 33(8). https://doi.org/10.1145/3680470
Raza, S., Shaban-Nejad, A., Dolatabadi, E., & Mamiya, H. (2024). Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review. IEEE Access, 12, 180815–180829. https://doi.org/10.1109/ACCESS.2024.3509353
Rosado Gómez, A. A., Calderón Benavides, M. L., & Espinosa, O. (2024). Data preprocessing to improve fairness in machine learning models: An application to the reintegration process of demobilized members of armed groups in Colombia. Applied Soft Computing, 152, 111193. https://doi.org/10.1016/J.ASOC.2023.111193
Tan, C. S., Gupta, A., Ong, Y. S., Pratama, M., Tan, P. S., & Lam, S. K. (2023). Pareto optimization with small data by learning across common objective spaces. Scientific Reports 2023 13:1, 13(1), 7842-. https://doi.org/10.1038/s41598-023-33414-6
Uddin, S., Ong, S., & Lu, H. (2022). Machine learning in project analytics: a data-driven framework and case study. Scientific Reports 2022 12:1, 12(1), 15252-. https://doi.org/10.1038/s41598-022-19728-x
Wan, M., Zha, D., Liu, N., & Zou, N. (2023). In-Processing Modeling Techniques for Machine Learning Fairness: A Survey. ACM Transactions on Knowledge Discovery from Data, 17(3). https://doi.org/10.1145/3551390
Wang, J., Qin, Z., Hsu, J., & Zhou, B. (2024). A fusion of machine learning algorithms and traditional statistical forecasting models for analyzing American healthcare expenditure. Healthcare Analytics, 5, 100312. https://doi.org/10.1016/J.HEALTH.2024.100312
Wu, H., Ma, C., Mitra, B., Diaz, F., & Liu, X. (2022). A Multi-Objective Optimization Framework for Multi-Stakeholder Fairness-Aware Recommendation. ACM Transactions on Information Systems, 41(2), 47. https://doi.org/10.1145/3564285
Xu, J., Xiao, Y., Wang, W. H., Ning, Y., Shenkman, E. A., Bian, J., & Wang, F. (2022). Algorithmic fairness in computational medicine. EBioMedicine, 84, 104250. https://doi.org/10.1016/j.ebiom.2022.104250
Yu, G., Ma, L., Wang, X., Du, W., Du, W., & Jin, Y. (2024). Towards fairness-aware multi-objective optimization. Complex & Intelligent Systems 2024 11:1, 11(1), 50-. https://doi.org/10.1007/S40747-024-01668-W
Yu, Z., Guindani, M., Grieco, S. F., Chen, L., Holmes, T. C., & Xu, X. (2022). Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron, 110(1), 21–35. https://doi.org/10.1016/j.neuron.2021.10.030
Zhang, R., Xu, X., Liu, K., Kong, L., Wang, X., Zhao, L., & Abuduwayiti, A. (2024). Does architectural design require single-objective or multi-objective optimisation? A critical choice with a comparative study between model-based algorithms and genetic algorithms. Frontiers of Architectural Research, 13(5), 1079–1094. https://doi.org/10.1016/J.FOAR.2024.03.010
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