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Explore a series of presentations from the FAT* 2019 conference focusing on economic models in fair machine learning. Delve into four talks that examine the intersection of economics, fairness, and machine learning algorithms. Begin with a moral framework for understanding fair ML through economic models of equality of opportunity. Then, investigate how access to population-level signaling can be a source of inequality. Next, learn about fair allocation through competitive equilibrium from generic incomes. Finally, discover fair algorithms for learning in allocation problems. Gain insights into how economic principles can be applied to create more equitable machine learning systems and resource allocation methods.