Fair-LS: A Group-Specific Clamping Factor for Fair Label Spreading in Graph-Based Semi-Supervised Learning - Oliveira, Willian Dihanster Gomes de; Berton, Lilian (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
Graph-based semi-supervised learning methods, such as Label Spreading, can propagate bias when the graph structure reflects group disparities. We propose Fair-LS, an extension of Label Spreading that introduces group-specific clamping factors to control label diffusion differently for privileged and unprivileged groups. Experiments on benchmark datasets, Adult Census Income, Compas Recidivism, and German Credit, show that Fair-LS could improve fairness metrics (Disparate Impact and Average Absolute Odds Difference) with minimal reduction in AUC-ROC. The results suggest that adapting propagation dynamics by group is an effective strategy to reduce bias in semi-supervised learning.