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

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


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

By

Miss Yue CUI


Abstract:

Ensuring fairness in machine learning is crucial for ethical decision-making 
and fostering societal trust. However, achieving fairness across diverse, 
real-world datasets presents significant challenges. Traditional 
controlling-based methods, such as fairness regularization, have been widely 
explored but often lack generalizability and struggle to accommodate 
multiple protected attributes or adapt to complex, heterogeneous data 
distributions. This thesis first addresses these limitations by proposing a 
universal fair representation learning framework that captures fairness 
w.r.t. an exponential number of constraints. Building on this foundation, we 
further explore fairness in dynamic and personalized settings. To enhance 
adaptability, we introduce a personalization-based approach utilizing a 
pre-train and fine-tune framework. This method ensures uniform performance 
across diverse target groups while addressing fairness misalignment in 
individualized scenarios. However, personalized fairness introduces new 
challenges, including susceptibility to strategic manipulation and conflicts 
among stakeholders with differing interests. To resolve these issues, we 
develop an incentive-based fairness alignment paradigm that integrates 
equilibrium-driven strategies. By incorporating momentary rewards into 
fairness mechanisms, this approach balances fairness objectives with 
stakeholder incentives, fostering more sustainable and robust fairness 
solutions in real-world applications. The findings of this thesis contribute 
to a more comprehensive understanding of fairness in machine learning, 
advancing both theoretical insights and practical applications in ethical AI 
development.


Date:                   Wednesday, 26 March 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 5504
                        Lifts 25/26

Chairman:               Dr. Ding PAN (PHYS)

Committee Members:      Prof. Xiaofang ZHOU (Supervisor)
                        Prof. Cunsheng DING
                        Prof. Qiong LUO
                        Dr. Can YANG (MATH)
                        Prof. Jeffery Xu YU (CUHK)