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             The rapid integration of artificial intelligence (AI) into various industries has introduced new challenges in 
             governance and regulation, particularly regarding the understanding of complex AI systems. A critical demand 
             from decision-makers is the ability to explain the results of machine learning models, which is essential for 
-            fostering trust and ensuring ethical AI practices. In this paper, we develop six distinct model-agnostic metrics 
+            fostering trust and ensuring ethical AI practices. In this paper, we develop nine distinct model-agnostic metrics 
             designed to quantify the extent to which model predictions can be explained. These metrics measure different aspects
             of model explainability, ranging from local importance, global importance, and surrogate predictions, allowing for a 
             comprehensive evaluation of how models generate their outputs. Furthermore, by computing our metrics, we can rank