This study investigates fairness in knowledge-aware recommender systems by evaluating their performance across both accuracy and fairness metrics. Using the MovieLens 1M dataset, we compare general, knowledge-aware, and fairness-optimized models through a custom RecBole-based pipeline. Results indicate knowledge-aware models offer some fairness benefits without major accuracy loss, though no model excels universally. Adjusting loss component weights reveals complex trade-offs and component importance, underscoring the need for nuanced fairness optimization.