Data Science Ph.D. Dissertation Proposal - "Preference Aggregation for Candidate Fairness" Kathleen Cachel

Ph.D. Dissertation Proposal

Kathleen Cachel, Ph.D. Candidate

Tuesday, August 22, 2023 | 1:00PM - 2:00PM

Location: Unity Hall 471

Dissertation Committee:

Professor Elke Rundensteiner. WPI (Advisor)

Professor Lane Harrison. WPI

Professor Andrew Trapp. WPI

Professor Nicholas Mattei. Tulane University

Title: 

Preference Aggregation for Candidate Fairness

Abstract: 

Traditional preference aggregation strategies combine the preferences of voters, expressed as rankings, into a single consensus ranking without consideration for how this ranking may unfairly affect marginalized groups (i.e., racial or gender). Developing fair preference aggregation strategies is critical due to their societal influence in domains such as information retrieval and social choice, along with their use in applications prioritizing job applicants, funding proposals, and scheduling medical patients. The research proposed in this dissertation explores the central question: How can we design preference aggregation strategies that are unbiased (fair) towards marginalized groups of ranked candidates, while still ensuring voter preferences are represented? I approach this question from a variety of ranked preference data types, exploring how different forms of preference data present unique fairness concerns, and require innovative bias mitigation strategies in generating an fair global consensus decision.