Ensuring Fairness Despite Differences in Environment

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dc.contributor.advisorEdmonds, Jeff
dc.contributor.advisorUrner, Ruth
dc.contributor.authorSingh, Karan Deep
dc.date.accessioned2021-07-06T12:52:23Z
dc.date.available2021-07-06T12:52:23Z
dc.date.copyright2021-04
dc.date.issued2021-07-06
dc.date.updated2021-07-06T12:52:22Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractSeveral 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.
dc.identifier.urihttp://hdl.handle.net/10315/38491
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectArtificial intelligence
dc.subject.keywordsMachine Learning
dc.subject.keywordsFairness Theory
dc.subject.keywordsEqual Opportunity
dc.subject.keywordsGroup Fairness
dc.titleEnsuring Fairness Despite Differences in Environment
dc.typeElectronic Thesis or Dissertation

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