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Exploring Diversity and Fairness in Machine Learning

dc.contributor.advisorDickerson, John Pen_US
dc.contributor.authorSchumann, Candiceen_US
dc.date.accessioned2020-09-25T05:36:04Z
dc.date.available2020-09-25T05:36:04Z
dc.date.issued2020en_US
dc.identifierhttps://doi.org/10.13016/0vev-63jr
dc.identifier.urihttp://hdl.handle.net/1903/26445
dc.description.abstractWith algorithms, artificial intelligence, and machine learning becoming ubiquitous in our society, we need to start thinking about the implications and ethical concerns of new machine learning models. In fact, two types of biases that impact machine learning models are social injustice bias (bias created by society) and measurement bias (bias created by unbalanced sampling). Biases against groups of individuals found in machine learning models can be mitigated through the use of diversity and fairness constraints. This dissertation introduces models to help humans make decisions by enforcing diversity and fairness constraints. This work starts with a call to action. Bias is rife in hiring, and since algorithms are being used in multiple companies to filter applicants, we need to pay special attention to this application. Inspired by this hiring application, I introduce new multi-armed bandit frameworks to help assign human resources in the hiring process while enforcing diversity through a submodular utility function. These frameworks increase diversity while using less resources compared to original admission decisions of the Computer Science graduate program at the University of Maryland. Moving outside of hiring I present a contextual multi-armed bandit algorithm that enforces group fairness by learning a societal bias term and correcting for it. This algorithm is tested on two real world datasets and shows marked improvement over other in-use algorithms. Additionally I take a look at fairness in traditional machine learning domain adaptation. I provide the first theoretical analysis of this setting and test the resulting model on two deal world datasets. Finally I explore extensions to my core work, delving into suicidality, comprehension of fairness definitions, and student evaluations.en_US
dc.language.isoenen_US
dc.titleExploring Diversity and Fairness in Machine Learningen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledDiversityen_US
dc.subject.pquncontrolledFairnessen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledMulti-armed banditsen_US


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