In this thesis, we study fairness and feedback effects in game theory and machine learning. In game theory and economics, financial or technological networks are analyzed for feedback effects. These studies analyze how the connectivity benefits or risk of contagious shocks affect the individual agents or the structure of the network formed by these rational agents. Towards this direction, in the first part of this thesis, we study a series of novel network formation games and analyze the structural properties of the equilibrium networks. Feedback effects can also occur in machine learning problems such as reinforcement learning or sequential allocation problems where the decisions of an algorithm over time can change the resources or actions available to the algorithm in the future as well as the environment in which the algorithm is operating. In the second part of this thesis, we study the effect of these feedback loops and ways to prevent them while also ensuring that the algorithm's actions and allocations satisfy natural notions of fairness. In particular we are interested in quantifying the cost of imposing fairness on learning algorithms.