In graph-based prediction settings, standard message passing in Graph Neural Networks often propagates correlations between neighborhoods and sensitive attributes, which results in biased node representations and unfair classification outcomes. In-processing mechanisms that modulate messages using protected-attribute relationships can enable fairness-aware representation learning by attenuating bias amplification during aggregation, as instantiated in this study through fairness-aware …
Category: User profiling
GNNFairViz: Visual analysis for fairness in graph neural networks
Graph neural networks are increasingly used to make predictions on relational data in settings such as social and financial networks. Yet, assessing whether these models treat demographic groups comparably is difficult because bias can arise not only from node attributes but also from the graph structure that drives message passing. By introducing a model-agnostic visual …
Toward a Responsible Fairness Analysis: From Binary to Multiclass and Multigroup Assessment in Graph Neural Network-Based User Modeling Tasks
By transitioning from binary to multiclass and multigroup fairness metrics, hidden biases in GNN-based user modeling are uncovered. Achieving true fairness requires fine-grained evaluation of real-world data distributions to ensure equity across all user groups and attributes. In an era dominated by artificial intelligence, ensuring fairness in automated decision-making has emerged as a critical priority. …
FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models
Modern user profiling approaches capture different forms of interactions with the data, from user-item to user-user relationships. Graph Neural Networks (GNNs) have become a natural way to model these behaviors and build efficient and effective user profiles. However, each GNN-based user profiling approach has its own way of processing information, thus creating heterogeneity that does …
Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives
This tutorial was presented at the UMAP ‘23 conference, with Erasmo Purificato and Ernesto William De Luca. On the tutorial’s website, you can find the slides and the notebooks of the hands-on parts. The proposed tutorial aimed to introduce the UMAP community to modern user profiling approaches leveraging graph neural networks (GNNs). We will begin …
Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling
User profiling approaches that model the interaction between users and items (behavioral user profiling) via Graph Neural Networks (GNNs) are unfair toward certain demographic groups. In a CIKM 2022 study, conducted with Erasmo Purificato and Ernesto William De Luca, we perform a beyond-accuracy analysis of the state-of-the-art approaches to assess the presence of disparate impact …