Researchers have developed a mathematical framework to evaluate explanations of machine-learning models and quantify how well people understand them. This approach aims to address the challenges faced by both black-box and white-box models. Historically, white-box models have struggled due to technical debt and constraints, leading to cascading errors. Black-box models, on the other hand, have gained popularity for their simplicity and effectiveness. The new framework seeks to make machine learning models more interpretable by decomposing black-box models into explainable predictor effects, thereby bridging the gap between complex models and user comprehension. Additionally, a dual-perspective approach to evaluating feature attribution methods has been proposed to enhance the interpretability of these models.