The growth of artificial intelligence applications requires to develop risk management models that can balance opportunities with risks. This chapter contributes to the development of artificial intelligence risk models presenting a set of integrated statistical metrics that can evaluate the “Sustainability,” “Accuracy,” “Fairness,” and “Explainability” of any artificial intelligence application, in line with the recommendations and regulations on the application of artificial intelligence that are being introduced around the world. The proposed metrics are consistent with each other, as they are all derived from a common underlying statistical methodology: the Lorenz curve. They are very general and can be applied to any machine learning method, regardless of the underlying data and model. Their empirical validity is assessed in the chapter by means of their practical application to a set of use cases. The applications reveal that the proposed metrics are more robust and more consistent with each other with respect to commonly used evaluation metrics such as mean squared error, area under the curve, Shapley values, and fairness parity measures. They can therefore be suggested to implement responsible and human-centered artificial intelligence frameworks.