Journal of Ai ML DL
Journal of Ai ML DL | Online ISSN 3070-2143
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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.
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