ETHICAL CONSTRAINTS AND BIAS MITIGATION IN FEDERATED LEARNINGENABLED EXPLAINABLE AI FOR PHARMACEUTICAL LOGISTICS

The rapid integration of Artificial Intelligence (AI) in pharmaceutical logistics has enhanced supply chainefficiency, predictive analytics, and decision-making. Federated Learning (FL) has emerged as a privacypreserving approach, enabling multiple stakeholders to collaboratively train models without sharing sensitive data.However, the decentralized nature of FL raises significant ethical concerns, including data security, fairness, andregulatory compliance. Additionally, bias in AI models, stemming from imbalanced data distributions, algorithmicbiases, and human-driven disparities, threatens equitable healthcare access and supply chain reliability.Explainable AI (XAI) offers a potential solution by providing transparency and interpretability in AI-drivenlogistics. However, achieving explainability in FL while ensuring fairness and mitigating bias presents technicaland ethical challenges. This study explores the ethical constraints of FL-enabled XAI in pharmaceutical logistics,focusing on privacy risks, algorithmic bias, and regulatory requirements such as GDPR and HIPAA. It furtherexamines state-of-the-art bias mitigation techniques, including fairness-aware model training, data rebalancingstrategies, and human-in-the-loop approaches. Through a comparative analysis of existing frameworks, thisresearch highlights the trade-offs between model performance, transparency, and fairness. Real-world case studiesdemonstrate how FL-enabled XAI is being applied in drug supply chains and demand forecasting, emphasizingbest practices for ethical AI deployment. The findings underscore the necessity of interdisciplinary collaborationamong policymakers, data scientists, and industry leaders to establish standardized guidelines for responsible AIuse. By addressing bias and ethical constraints in FL, this research contributes to the development of equitableand transparent pharmaceutical logistics, ensuring that AI-driven decisions align with ethical standards andregulatory mandates.