Fairness in Algorithmic Decision Making

We examine quantitative models and a variety of fairness goals in settings where machine learning algorithms make crucial decisions. We will look at questions such as the reasons for unfairness, the price one has to pay to achieve fairness, and the incentives necessary to induce myopic agents to make their decision-making process fair. Our approach is to pose these problems as problems in optimization under uncertainty.

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