A MULTI-DIMENSIONAL FRAMEWORK FOR ADDRESSING BIAS AND FAIRNESS IN MACHINE LEARNING FOR HEALTHCARE ANALYTICS

Abstract Machine learning (ML) systems increasingly influence clinical decision-making, yet algorithmic bias poses significant risks to patient safety and health equity. A commercial risk prediction algorithm underestimated illness severity for 70,000 Black patients annually. Current approaches inadequately address interconnected ethical, social, sustainability, and regulatory dimensions. This study develops a comprehensive four-dimensional framework integrating ethical principles, social implications, environmental sustainability, and regulatory compliance. Built upon ISO/IEC 42001 and IEEE standards, it incorporates GDPR, HIPAA, and EU AI Act requirements. Three core strategies are proposed: fairness-aware algorithms, transparent auditing, and inclusive development teams. The framework reduces disparate impact by 46% while maintaining clinical accuracy. Implementation guidance addresses fairness-accuracy trade-offs, resource constraints, data limitations, and regulatory complexity. Healthcare organizations can implement foundational recommendations immediately, progressing through intermediate to advanced capabilities, enabling ML deployment that enhances rather than undermines health equity. Keywords machine learning, healthcare analytics, algorithmic bias, fairness, health equity, regulatory compliance, sustainability