Many ranking and social recommender algorithms are based on data from social networks. For example, social media platforms like Twitter or LinkedIn use the information on social networks to rank people and recommend new social links to users. These networks, generated by people, are driven by fundamental social mechanisms such as popularity and homophily and often contain different sociodemographic characteristics of people. These attributes play an important role in the way individuals interact with others and thus determine the structure of networks. In addition, the structure of networks plays a crucial role in dynamic processes in networks such as information dissemination, formation and development of prejudices, norms and culture. However, very little is known about the impact of networks. Structure on algorithms, the extent to which machine learning methods reinforce social biases and practical approaches to mitigating algorithmic biases. The overall goal of this project is to examine the role of recommendation and ranking algorithms in reinforcing bias in social networks with a particular focus on minorities. The results of this project will help develop better algorithms that are fair to minorities.