LIFECYCLE GOVERNANCE FOR EXPLAINABLE AI IN PHARMACEUTICAL SUPPLY CHAINS: A FRAMEWORK FOR CONTINUOUS VALIDATION, BIAS AUDITING, AND EQUITABLE HEALTHCARE DELIVERY

The integration of artificial intelligence (AI) in pharmaceutical supply chains promises unprecedented gains in
efficiency, demand forecasting, and therapeutic distribution. However, the opacity of many AI systems—
especially those embedded in procurement optimization, quality control, and inventory prediction—raises serious
concerns about fairness, accountability, and bias propagation in healthcare access. As these technologies
increasingly influence clinical decision-making, drug availability, and distribution logistics, ensuring transparency
and ethical compliance across their entire lifecycle becomes a public health imperative. This paper proposes a
comprehensive lifecycle governance framework for implementing explainable AI (XAI) in pharmaceutical supply
chains. Moving beyond static compliance models, the framework introduces continuous validation checkpoints
that assess model fidelity across design, deployment, and post-deployment phases. Emphasis is placed on bias
auditing, which evaluates disparities in drug distribution across socio-economic and geographic lines, ensuring
algorithmic decisions do not reinforce structural inequalities. We further outline mechanisms for stakeholder
participation, integrating insights from pharmacists, healthcare regulators, supply chain managers, and AI
ethicists. Technical approaches such as SHAP values, counterfactual analysis, and attention mechanisms are
contextualized within governance protocols to enhance model transparency. A case-based illustration
demonstrates how this framework can be applied to a vaccine supply chain model, showing improvements in
fairness, responsiveness, and trustworthiness. By embedding explainability and oversight across the AI lifecycle,
the proposed model fosters equitable, safe, and accountable supply chain ecosystems. Ultimately, such governance
is essential for aligning AI adoption with the broader goals of universal health coverage, pharmaceutical justice,
and ethical AI deployment in critical healthcare infrastructures