Beyond Predictive Performance: An Explainability–Governance Score Framework for Multidimensional Machine Learning Model Selection

Machine learning models are increasingly deployed in high-stakes decision systems where predictive performance alone may provide an insufficient basis for responsible model selection. Although explainable artificial intelligence (XAI) offers techniques for interpreting model behavior, existing evaluation approaches often assess explanation properties independently and provide limited support for integrated model selection. This study proposes the Explainability–Governance Score (EGS) Framework, a multidimensional decision-support methodology integrating four complementary dimensions: Fidelity, local predictive Stability, Counterfactual Actionability, and Governance Alignment. The framework combines established XAI techniques, practical recourse, and governance-oriented criteria into a composite evaluation of candidate models. It is empirically evaluated using five machine learning models across three high-stakes domains represented by the German Credit, Pima Indians Diabetes, and COMPAS datasets. Under the primary BPS specification, predictive evaluation ranked XGBoost, Decision Tree, and Random Forest highest in Credit Risk, Healthcare, and Criminal Justice, respectively. Under the multidimensional EGS evaluation, Logistic Regression achieved the highest EGS in Credit Risk (0.915) and Healthcare (0.915), while Decision Tree achieved the highest EGS in Criminal Justice (0.886). The identity of the highest-EGS model remained unchanged across all six prespecified weighting configurations in each domain. These findings demonstrate that multidimensional EGS evaluation can materially influence candidate-model rankings relative to predictive evaluation and provide a structured, reproducible basis for evaluating candidate models beyond predictive performance alone.