The profiles from data-driven profiling applications are a model of the reality. The interpretability of these profiles for end users, e.g. policymakers, is often far from trivial. How and why these models are obtained by the applications are often regarded as a black box. In recent years several profiling applications used by public organizations have led to wrong interpretations of the obtained models and impacted individuals and society adversely. Hence, the research focus has increasingly shifted towards dealing with the trust and interpretability issues of the models. In support of a more careful and proper interpretation of these models, several scholars have advocated a glass box approach that aims at making these models more transparent to end users. In this paper, we operationalize the glass box approach for a Genetic Algorithm (GA) based profiling application. To enhance the interpretability of the models provided by the application, we aim at facilitating the interaction of domain experts with the models. Hereby domain experts can gain insight to the evolvement of the profiles and what happens to the profiles if we change or add a new pieces of information. Adding such an interactive visualization provides more transparency about the derived models, making them more understandable for end users and policymakers. As a result, they can better assess and explain the consequences of those models when they apply to practice.