Ethical Frameworks for Automated Decision-Making in Healthcare: Balancing Innovation and Equity in ML-Driven Resource Allocation

Abstract

Automated decision-making systems powered by artificial intelligence (AI) and machine learning (ML) are transforming healthcare resource allocation, offering unprecedented opportunities to enhance efficiency while raising profound ethical questions. The integration of these technologies introduces fundamental tensions between operational optimization and equitable healthcare delivery across diverse populations. This article explores how values become embedded throughout the data engineering pipeline, from collection practices that reflect historical inequities to feature selection processes that may inadvertently penalize disadvantaged communities. Governance frameworks balancing automation with human oversight are proposed, emphasizing tiered autonomy models, override mechanisms, and continuous monitoring for disparities. The article concludes that achieving responsible implementation requires explicit recognition of value-laden technical decisions and structured approaches to balancing innovation with equity. Only through thoughtful consideration of these ethical dimensions can healthcare organizations harness the transformative potential of ML-driven resource allocation while upholding core principles of medical ethics and ensuring equitable care delivery across all patient populations.