Algorithmic Neutrality in Healthcare AI

Abstract

This chapter critically examines the assumption of algorithmic neutrality in healthcare artificial intelligence (AI). Although AI is often promoted as a driver of accuracy, efficiency, and equity, evidence shows that it can reinforce disparities through biases in data, model design, and implementation. Based on a review of biomedical, computational, and policy literature from 2017 to 2025, the chapter highlights how underrepresentation of marginalized populations, limited validation across contexts, opaque algorithms, and weak stakeholder engagement contribute to inequitable outcomes. It argues that technical bias mitigation alone is insufficient without addressing structural inequalities. Emphasis is placed on participatory and justice-oriented frameworks, transparent audits, accountable governance, and inclusive design. Embedding equity and accountability across the AI lifecycle is presented as essential for ensuring digital innovation advances health justice.