The fields of higher education research, social network research, machine learning, and graph representation learning are experiencing significant advancements in understanding and promoting equity and fairness. A common theme among these areas is the importance of considering structural and historical contexts, ensuring equitable influence distribution, and developing methods that accommodate heterogeneous tasks and incomplete supervision.
In higher education research, studies have highlighted the importance of considering the differential effects of structural filters, such as the Common Basic Cycle, on student progression. The use of longitudinal administrative records and advanced data analysis techniques has enabled researchers to uncover patterns of stratification and inequality in university systems. Notably, the concept of quantised academic mobility has emerged, which introduces a new framework for understanding complex student trajectories.
In social network research, ensuring equitable influence distribution across all communities, regardless of protected attributes, is a growing concern. The phenomenon of filter bubbles and their negative effects, such as group polarization, is being explored. Researchers are also working to quantify structural polarization in social and information networks.
Machine learning is moving towards greater emphasis on fairness and explainability, with the development of fairness-aware multitask learning frameworks and probabilistic neuro-symbolic reasoning. These advancements have shown promising results in achieving substantial fairness gains while maintaining superior task utility.
Graph representation learning is also focused on developing more robust and fair models, particularly in scenarios where sensitive attributes are incomplete or missing. Methods such as contrastive learning, hierarchical topological granularity, and hyperbolic continuous structural entropy have shown promising results in capturing meaningful graph representations.
Some notable papers in these areas include Differential Filtering in a Common Basic Cycle, Quantised Academic Mobility, DQ4FairIM, FairMT, and An Information Geometric Approach to Fairness With Equalized Odds Constraint. These studies demonstrate the importance of considering accuracy, fairness, and explainability jointly in model assessment, rather than in isolation. Overall, the push towards equity and fairness in these emerging research areas is expected to have a significant impact on promoting inclusivity and diversity in various fields.
