Recommender systems have transformed how users access information, particularly in the news domain. While these systems enhance personalization, they also introduce fairness concerns, potentially limiting users’ exposure to diverse perspectives. This experimental study aims to evaluate fairness across various recommendation algorithms by comparing collaborative and neural network-based approaches. To assess performance, both classical evaluation metrics, together with bias and fairness-aware metrics, such as Bias Disparity, Ranking-based Equal Opportunity, and Average Recommendation Popularity, are considered. The Adressa dataset is used to analyze recommendation behavior and their impact when using various user and item attributes. The results highlight important variations in fairness distribution across different algorithms, underscoring the necessity of incorporating fairness considerations into recommender system design.