Social networks are complex by nature, and the position of individuals in them is consequential for a variety of decision-making processes. Connections between individuals are driven by different tie formation mechanisms that can explain the various structures embedded in social networks. These driving mechanisms are often the key ingredients of social and algorithmic processes, which unintentionally may create or amplify existing inequalities and harm certain (groups of) people. The scientific community has tried to mitigate these issues by measuring fairness for individuals and groups. However, they often overlook personal networks as a potential source of bias in decision-making, leading to a lack of research assessing their far-reaching impact. Further, existing definitions of fairness are often centered around justice and ignore potential conflicts with other moral goals. This leads to three major research gaps: First, bias mitigation research focusing on network effects is not well understood. Second, fairness is often tackled as a distributive justice problem and ignores other moral goals such as well-being, and autonomy. Third, the possibility of satisfying multiple moral goals simultaneously, for one or multiple interconnected stakeholders is a topic that requires further study. In this talk I will introduce a new area of research where ethical decision-making and a deeper understanding of people’s complex and multifaceted relationships are essential for achieving fairness in complex societies while upholding multiple moral goals.