A MULTI-DIMENSIONAL FRAMEWORK FOR ADDRESSING BIAS AND FAIRNESS IN MACHINE LEARNING FOR HEALTHCARE ANALYTICS | IJCT Volume 13 – Issue 1 | IJCT-V13I1P8
International Journal of Computer Techniques
ISSN 2394-2231
Author
CHINONSO JOB, OGBU, JOY ONYINYEOMA, ONWE, FESTUS CHIJIOKE
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
Conclusion
Machine learning systems in healthcare hold tremendous promise but require proactive, systematic attention to bias and fairness. This paper presents a multi-dimensional framework integrating ethical, social, sustainability, and regulatory considerations with concrete implementation strategies. The framework’s core contribution demonstrates that effective bias mitigation requires coordinated action across technical practices, organizational processes, cultural factors, and regulatory compliance.
Healthcare organizations can immediately apply framework recommendations, beginning with governance structures and basic auditing, then progressively building advanced capabilities. The phased roadmap provides actionable guidance for organizations at various capability levels. As ML becomes increasingly embedded in healthcare delivery, the decisions made now about fairness and accountability will shape health equity for decades to come.
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How to Cite This Paper
CHINONSO JOB, OGBU, JOY ONYINYEOMA, ONWE, FESTUS CHIJIOKE (2025). A MULTI-DIMENSIONAL FRAMEWORK FOR ADDRESSING BIAS AND FAIRNESS IN MACHINE LEARNING FOR HEALTHCARE ANALYTICS. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
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