Journal of Ai ML DL
Journal of Ai ML DL | Online ISSN 3070-2143
0
Citations
13.3k
Views
12
Articles
Volume 2 Number 1 2026
RESEARCH ARTICLE (Open Access)
Explainable AI Framework for Detecting and Reducing Health Disparities in Healthcare Supply Chains
Fahad Ahmed 1*, Shaid Hasan 2, Adib Hossain 3, Khandaker Ataur Rahman 2
Journal of Ai ML DL 2 (1) 1-8 https://doi.org/10.25163/ai.2110685
Submitted: 30 November 2025 Revised: 13 February 2026 Accepted: 17 February 2026 Published: 18 February 2026
Abstract
Yet health disparities in terms of access to healthcare supplies and services continue to be a reality in the healthcare system in spite of improvements in healthcare logistics and optimization. Today’s healthcare supply chains increasingly use machine learning and optimization techniques to forecast demand and allocate supplies; yet again, they can be considered “black boxes” that can perpetuate health disparities in unintended ways. This article presents a framework called Explainable and Fairness-Aware Artificial Intelligence (XAI) in healthcare supply chains. The framework uses fairness-aware machine learning models, such as demand/risk prediction, combined with explainability tools such as SHAP or LIME, that can uncover the underlying causes of allocation inequality. These are then incorporated into an optimization model that balances efficiency goals with constraints on equity, such as ensuring that regions with the highest vulnerability are allocated the most.The proposed method, through its ability to transform the results of explainability models into actionable optimization constraints, extends the traditional bias detection process towards correcting inequities with real-world resource constraints. The method is immediately relevant and applicable to the federal equity and healthcare priorities of the United States, as well as the Healthy People 2030 and the HHS Equity Action Plan, due to its ability to facilitate transparent and accountable decision-making within healthcare logistics settings. This research presents a new and innovative method of unifying AI transparency and supply chain optimization for equitable healthcare delivery.
Keywords: Explainable Artificial Intelligence (XAI), Healthcare Supply Chain Optimization, Algorithmic Fairness, Health Disparities, Fairness-Aware Machine Learning, Equity-Constrained Resource Allocation.
References
Choi, T.-M. (2021). Risk analysis in logistics systems: A research agenda. Journal: Transportation Research Part E: Logistics and Transportation Review https://doi.org/10.1016/j.tre.2021.102301
Dasgupta, S., Bowen, V. B., Leidner, A., et al. (2020). Association between social vulnerability and COVID-19 vaccination coverage. Journal: MMWR Morbidity and Mortality Weekly Report
https://doi.org/10.15585/mmwr.mm6944e1
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv: https://doi.org/10.48550/arXiv.1702.08608
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
Jean-Jacques, M., & Bauchner, H. (2021). Vaccine distribution—equity left behind? JAMA, (Journal of the American Medical Association) 325(9), 829–830. https://doi.org/10.1001/jama.2021.1200
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, https://doi.org/10.48550/arXiv.1705.07874
Mehrabi, N., Morstatter, F., Saxena, N., et al. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, Conference paper 54(6), 1–35. https://doi.org/10.48550/arXiv.1705.07874
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Journal: ACM Computing Surveys, 366(6464), 447–453. https://doi.org/10.1145/3457607
U.S. Department of Health and Human Services. (2021). Journal of science. HHS Equity Action Plan. https://doi.org/10.1126/science.aax2342
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. Journal: California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31
Bertsimas, D., Farias, V. F., & Trichakis, N. (2020). Fairness, efficiency, and flexibility in organ allocation. Journal of Management Science, 66(7), 3011–3027. https://doi.org/10.1287/mnsc.2019.3362
Braveman, P., Egerter, S., & Williams, D. R. (2011). The social determinants of health: Coming of age.journal of Annual Review of Public Health, 32, 381–398. https://doi.org/10.1146/annurev-publhealth-031210-101218
Corbett-Davies, S., & Goel, S. (2018). The measure and mismeasure of fairness. arXiv preprint arXiv:1808.00023. https://doi.org/10.48550/arXiv.1808.00023
Daskin, M. S., & Dean, L. K. (2004). Location of health care facilities. Journal of Operations Research, 52(1), 18–34. https://doi.org/10.1287/opre.1030.0075
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://doi.org/10.48550/arXiv.1702.08608
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The journal of Lancet Digital Health, 3(11), e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Journal of Advances in Neural Information Processing Systems, https://doi.org/10.48550/arXiv.1705.07874
Marmot, M., Allen, J., Goldblatt, P., Herd, E., & Morrison, J. (2020). Health equity in England: The Marmot review 10 years on. BMJ Publishing. https://doi.org/10.1136/bmj.m693
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. Journal of ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2019). An empirical characterization of fair machine learning for clinical risk prediction. Journal of Biomedical Informatics, 99, 103292. https://doi.org/10.1016/j.jbi.2019.103292
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD, 1135–1144. https://doi.org/10.1145/2939672.2939778
Islam, Md Mainul and Zerine, Ismoth and Rahman, Md Arifur and Islam, Md Saiful and Ahmed, Md Yousuf, AI-Driven Fraud Detection in Financial Transactions -Using Machine Learning and Deep Learning to Detect Anomalies and Fraudulent Activities in Banking and E-Commerce Transactions (December 29, 2024). Available at SSRN. https://doi.org/10.2139/ssrn.5287281
Uscher-Pines, L., et al. (2021). Barriers and facilitators to equitable COVID-19 vaccine distribution. Health Affairs, 40(10), 1537–1545. https://doi.org/10.1377/hlthaff.2021.00949
Zhang, Y., & Shah, N. H. (2023). Algorithmic fairness in healthcare: A review and recommendations. NPJ Digital Medicine, 6(1), 1–9. https://doi.org/10.1038/s41746-023-00786-1
Zhang, H., Luo, X., & Song, H. (2022). Machine learning–driven healthcare supply chain management: A review. Computers & Industrial Engineering, 167, 107994. https://doi.org/10.1016/j.cie.2022.107994
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31
Bertsimas, D., Farias, V. F., & Trichakis, N. (2020). Fairness, efficiency, and flexibility in resource allocation. Management Science, 66(7), 3011–3027. https://doi.org/10.48550/arXiv.1808.00023
Corbett-Davies, S., & Goel, S. (2018). The measure and mismeasure of fairness. arXiv preprint arXiv:1808.00023. https://doi.org/10.48550/arXiv.1808.00023
Zerine, I., Islam, M. S., Ahmad, M. Y., Islam, M. M., Biswas, Y. A. (2023). "AI-Driven Supply Chain Resilience: Integrating Reinforcement Learning and Predictive Analytics for Proactive Disruption Management", Business and Social Sciences, 1(1),1-12,10343. https://doi.org/10.25163/business.1110343
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://doi.org/10.48550/arXiv.1702.08608
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866–872. https://doi.org/10.7326/M18-1990
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD, 1135–1144.
Zhang, Y., & Shah, N. H. (2023). Algorithmic fairness in healthcare: A review and recommendations. NPJ Digital Medicine, 6(1), 1–9. https://doi.org/10.1038/s41746-023-00786-1
Zerine, I., Rahman, T., Ahmad, M. Y., Biswas, Y., & Islam, M. M. (2025). Enhancing public health supply chain forecasting using machine learning for crisis preparedness and system resilience. International Journal of Communication Networks and Information Security, 17(4), 82–98.
Bertsimas, D., Farias, V. F., & Trichakis, N. (2020). Fairness, efficiency, and flexibility in resource allocation. Management Science, 66(7), 3011–3027. https://doi.org/10.1287/mnsc.2019.3362
Braveman, P., Egerter, S., & Williams, D. R. (2011). The social determinants of health: Coming of age. Annual Review of Public Health, 32, 381–398. https://doi.org/10.1146/annurev-publhealth-031210-101218
Daskin, M. S., & Dean, L. K. (2004). Location of health care facilities. Operations Research, 52(1), 18–34. https://doi.org/10.1287/opre.1030.0075