Unlearning Algorithmic Biases over Graphs
O. Deniz Kose, Gonzalo Mateos, Yanning Shen
TL;DR
This work tackles algorithmic bias amplification in graph-based learning under data-removal policies by enabling certified, training-free bias mitigation through graph unlearning. It introduces a Newton-update-based unlearning procedure applied to pre-trained graph models and provides two bias-clarifying unlearning strategies: node feature unlearning and structural unlearning (edges and nodes), each accompanied by provable bounds and bias-reduction mechanisms. The key contributions include sublinear scaling guarantees for feature unlearning, principled bias scores for structural forgetting, and experimental evidence across multiple real networks showing substantial fairness improvements (up to ~75%) with large runtime savings (10×–20× faster than retraining). Overall, the approach offers a practical, low-cost post-processing option to enhance fairness in graph ML while remaining compliant with data-deletion regulations.
Abstract
The growing enforcement of the right to be forgotten regulations has propelled recent advances in certified (graph) unlearning strategies to comply with data removal requests from deployed machine learning (ML) models. Motivated by the well-documented bias amplification predicament inherent to graph data, here we take a fresh look at graph unlearning and leverage it as a bias mitigation tool. Given a pre-trained graph ML model, we develop a training-free unlearning procedure that offers certifiable bias mitigation via a single-step Newton update on the model weights. This way, we contribute a computationally lightweight alternative to the prevalent training- and optimization-based fairness enhancement approaches, with quantifiable performance guarantees. We first develop a novel fairness-aware nodal feature unlearning strategy along with refined certified unlearning bounds for this setting, whose impact extends beyond the realm of graph unlearning. We then design structural unlearning methods endowed with principled selection mechanisms over nodes and edges informed by rigorous bias analyses. Unlearning these judiciously selected elements can mitigate algorithmic biases with minimal impact on downstream utility (e.g., node classification accuracy). Experimental results over real networks corroborate the bias mitigation efficacy of our unlearning strategies, and delineate markedly favorable utility-complexity trade-offs relative to retraining from scratch using augmented graph data obtained via removals.