Fairness in recommendation systems is a critical area of study, particularly when addressing group disparities based on sensitive attributes such as gender, age, activity levels, or user location. This study also explores latent groups identified through hierarchical clustering techniques. The goal is to assess group unfairness across various clustering configurations and collaborative filtering strategies to promote equitable and inclusive recommendation systems. We applied collaborative filtering techniques, including ALS, KNN, and NMF, and evaluated group unfairness using metrics such as Rgrp for different clustering configurations (e.g., gender, age, activity level, location, and hierarchical clustering) in two datasets: MovieLens and Amazon Books. Hierarchical clustering yielded the highest group unfairness, with ALS and NMF reaching Rgrp values of 0.0062 and 0.0049 in MovieLens, and NMF and KNN peaking at 0.0972 and 0.0220 in Amazon Books. These results reveal significant fairness disparities across both latent and observable user groups, reinforcing the importance of selecting appropriate filtering strategies and clustering methods to build fair and inclusive recommendation systems.