Ethical Frameworks for Bias Mitigation in AI Algorithms for Health Equity Assessment

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) in healthcare promises transformative benefits for diagnosis, prognosis, and treatment optimization. However, the increasing reliance on algorithmic decision-making has surfaced systemic biases, particularly in health equity assessment, leading to disparities in care delivery and outcomes. This paper presents a comprehensive ethical framework for bias mitigation in AI algorithms, emphasizing methodological, computational, and governance approaches. Drawing on theoretical foundations, regulatory perspectives, and practical healthcare applications, the study explores strategies to detect, quantify, and mitigate algorithmic bias while ensuring fairness, transparency, and accountability. Case studies in precision medicine and clinical decision support highlight the application of these frameworks. The findings aim to guide researchers, clinicians, and policymakers in deploying equitable AI solutions that reinforce health equity and patient-centered care.