The integration of artificial intelligence (AI) into healthcare systems demands governance frameworks that reconcile technical accountability with health equity imperatives. This chapter proposes the Stratified Healthcare Accountability Protocol for Equity (SHAPEquity), a governance framework that implements explainable AI (XAI) methodologies—including SHAP and LIME—into regulatory compliance, clinical validation, and equity auditing. The analysis of systemic failures such as the Epic sepsis model (AUROC 0.47) and a comparative assessment of global regulations—such as the EU AI Act, FDA, and WHO guidelines—reveals critical governance gaps that perpetuate algorithmic harm in marginalized populations. SHAPEquity introduces three core innovations for global governance and achieving Sustainable Development Goal 3 (SDG 3). First, a dynamic transparency matrix aligns XAI techniques with clinical risk tiers. Second, a protocol for health equity assurance establishes legal standards for fairness such as counterfactual equity metrics. Third, institutional accountability mechanisms recognize transparent AI access as a health justice imperative. The framework provides policymakers with tools to mitigate algorithmic neocolonialism and advance equitable care delivery. This work bridges legal theory, clinical practice, and AI engineering to operationalize health justice in algorithmically mediated healthcare.