Fairness-alignment in Data-centric Machine Learning: From Regularization to Equilibrium

PhD Thesis Proposal Defense


Title: "Fairness-alignment in Data-centric Machine Learning: From 
Regularization to Equilibrium"

by

Miss Yue CUI


Abstract:

Fairness in machine learning is essential for ethical decision-making and 
societal trust, yet achieving it in diverse, real-world datasets remains 
challenging. Traditional controlling-based methods, such as fairness 
regularization, have been extensively studied for mitigating bias. However, 
these methods often lack generalizability and struggle to incorporate a 
large number of protected attributes or adapt to complex, heterogeneous data 
distributions. To address these limitations, we propose a universal fair 
representation learning approach that captures fairness constraints in a 
more generalized and scalable manner. While regularization-based methods 
provide foundational solutions, they fail to address fairness alignment in 
dynamic or personalized settings. To this end, we introduce 
personalization-based approaches, a pre-train and fine-tune framework, which 
offer tailored solutions to ensure performance uniformity across diverse 
target objects. However, these approaches come with new challenges, 
including vulnerability to strategic manipulation and conflicting fairness 
priorities among stakeholders. To mitigate these challenges, we explore a 
novel incentive-based fairness alignment paradigm that emphasizes 
equilibrium-driven strategies. By integrating momentary rewards into 
fairness mechanisms, this paradigm balances fairness and stakeholder 
incentives, paving the way for more robust and sustainable solutions in 
real-world applications.


Date:                   Wednesday, 5 February 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 4472
                        Lifts 25/26

Committee Members:      Prof. Xiaofang Zhou (Supervisor)
                        Prof. Qiong Luo (Chairperson)
                        Prof. Cunsheng Ding
                        Dr. Lei Li