Modern recommender systems had their roots in the early 1990s when they were primarily used experimentally for information filtering and personal email. Person- alized recommendations are commonplace today, 30 years later, and research in this very successful machine learning research field is booming faster than ever. Besides the fact that recommendation systems enhance a company's revenue, they also help with improving user experience, by helping users to discover new and relevant prod- ucts or content. In the context of increased usage of the recommendation systems, different concerns were raised regarding them, and one of the most pro-eminent is fairness if a system is biased or unrepresentative of the population it is intended to serve, the recommendations generated by the system may be unfair or discrimina- tory. This work's objective is the study of fairness but on recommendation systems with visual awareness, which are used more frequently in the current state of the domain. The goal of this work has been the analysis of visually aware recommender systems, to determine if a bias exists in the proposed recommendations. The analysis denotes that this bias exists, and different proposals, one pre-processing approach, and one post-processing have been provided to mitigate the existing bias. The results of the proposed solutions have an effect of bias by reducing it, however, such reduction is a set of trade-offs that have to be taken into account. Overall, this work highlights the importance of fairness and provides practical solutions for the mitigation of biased behavior that may be present.