To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier

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dc.contributor.advisorAllen, Genevera
dc.contributor.committeeMemberBalakrishnan, Guha
dc.contributor.committeeMemberSabharwal, Ashutosh
dc.creatorLittle, Camille
dc.date.accessioned2023-08-09T19:15:58Z
dc.date.available2023-08-09T19:15:58Z
dc.date.created2023-05
dc.date.issued2023-04-21
dc.date.submittedMay 2023
dc.date.updated2023-08-09T19:15:58Z
dc.description.abstractLarge-scale machine learning systems are being deployed to aid in making critical decisions in various areas of our society, including criminal justice, finance, healthcare, and education. In many cases, however, systems trained on biased data reflect or exacerbate these biases leading to unfair algorithms that disadvantage protected classes based on gender, race, sexual orientation, age, or nationality. Unfortunately, improved fairness often comes at the expense of model accuracy. Existing works addressing the fairness-accuracy tradeoff report fairness and accuracy separately at a single hyperparameter, making it impossible to compare performance between models and model families across the entire frontier. Taking inspiration from the AUC-ROC literature, we develop a method for identifying (TAF) and measuring (Fairness-AUC) the Pareto fairness-accuracy frontier. Further, we ask: Is it possible to expand the empirical Pareto frontier and thus improve the Fairness-AUC for a given collection of fitted models? We answer affirmatively by developing a novel fair model stacking framework, FairStacks, that solves a convex program to maximize the accuracy of the model ensemble subject to a relaxed bias constraint. We show that optimizing with FairStacks always expands the empirical Pareto frontier and improves the Fairness-AUC; we additionally study other theoretical properties of our proposed approach. Finally, we empirically validate TAF, Fairness-AUC, and FairStacks through studies on several real benchmark data sets, showing that FairStacks leads to major improvements in Fairness-AUC that outperform existing algorithmic fairness approaches.
dc.format.mimetypeapplication/pdf
dc.identifier.citationLittle, Camille. "To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier." (2023) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/115174">https://hdl.handle.net/1911/115174</a>.
dc.identifier.urihttps://hdl.handle.net/1911/115174
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectAlgorithmic Fairness
dc.titleTo the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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