Edmonds, JeffUrner, RuthSingh, Karan Deep2021-07-062021-07-062021-042021-07-06http://hdl.handle.net/10315/38491Several fairness definitions have been proposed in the machine learning literature to rectify the issue of demographic groups being treated differently. Given the substantial research in the field, this work aims to provide an entry-level overview of the common definitions and metrics that are essential for a novice reader in the field. In addition, we propose a theorem, where we look at different population distributions and conditions under which our claim holds, that is the disadvantaged individual is expected to be more talented than the similarly performing advantaged individual. Finally, this work summarizes the six research works and discusses whether the result of our theorem is consistent in each of the research work's model settings, culminating in a discussion of how all the authors view the world in terms of a group's talent distribution.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Artificial intelligenceElectronic Thesis or Dissertation2021-07-06Machine LearningFairness TheoryEqual OpportunityGroup Fairness