Fairness is currently becoming a necessary dimension to consider in contemporary artificial intelligence(AI)-based systems, according to the recent Ethics Guidelines for a Trustworthy AI. Being recommender systems popular applications that incorporate AI in a larger or lesser extent, the literature analysis identifies a research gap related to the exploration of demographic parity fairness in the group recommendation scenario. The current work is focused on this gap, developing a group recommendation framework that has as main novelty the measuring of the consumer fairness taking into account the presence of advantaged and disadvantaged class of users. Experimental studies are developed for measuring the performance of the proposal in a real recommendations scenario, illustrating that it is able to distinguish different fairness levels across the delivered recommendations.