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Rapid advancements in artificial intelligence (AI) technology have broughtabout a plethora of new challenges in terms of governance and regulation. AIsystems are being integrated into various industries and sectors, creating ademand from decision-makers to possess a comprehensive and nuancedunderstanding of the capabilities and limitations of these systems. Onecritical aspect of this demand is the ability to explain the results of machinelearning models, which is crucial to promoting transparency and trust in AIsystems, as well as fundamental in helping machine learning models to betrained ethically. In this paper, we present novel quantitative metricsframeworks for interpreting the predictions of classifier and regressor models.The proposed metrics are model agnostic and are defined in order to be able toquantify: i. the interpretability factors based on global and local featureimportance distributions; ii. the variability of feature impact on the modeloutput; and iii. the complexity of feature interactions within model decisions.We employ publicly available datasets to apply our proposed metrics to variousmachine learning models focused on predicting customers' credit risk(classification task) and real estate price valuation (regression task). Theresults expose how these metrics can provide a more comprehensive understandingof model predictions and facilitate better communication betweendecision-makers and stakeholders, thereby increasing the overall transparencyand accountability of AI systems.

Local and Global Explainability Metrics for Machine Learning Predictions