Multi robot systems are growing rapidly, expanding beyond factories and into our daily lives. Because these systems exist within social contexts, it is crucial that their decisions fairly treat those being served. Therefore, this thesis leverages principles of fairness to create definitions and measures that are relevant to the application of multi robot systems in societal settings. These definitions are used to investigate the impact of existing approaches and to develop new methods. A core principle of fairness examined in this thesis is perspective, which considers whose outcomes are being compared. We start with a narrow perspective of fairness, comparing individuals' outcomes. We expand this perspective throughout the thesis to study fairness between populations. Our presented methods mirror this progression: we start with algorithms that make decisions based on individual attributes and end with small robot teams that serve large populations. We demonstrate the use and impact of our methods in applications like emergency delivery and disaster response.