Develop a comprehensive framework for explaining predictions from a complex ensemble model used for loan approval decisions. Your framework should incorporate: (1) global interpretation methods to understand overall model behavior; (2) local interpretation methods for individual predictions; (3) feature importance analysis with multiple techniques; (4) counterfactual explanations generating actionable alternatives; (5) handling of correlated features in explanations; (6) validation of explanations through quantitative metrics; (7) consideration of fairness and bias implications; (8) presentation strategy for different stakeholders (technical and non-technical). Include implementation details and discuss limitations of each approach.