Fair CoVariance Neural Networks

2024-09-13

arXiv.org

AI Summary

In few-shot learning scenarios, conventional covariance modeling induces group unfairness—e.g., algorithmic overfitting to sensitive attributes—compromising statistical reliability and fairness robustness. Method: We propose FairGCov, a Fair Graph-based Covariance Neural Network that jointly models fair covariance estimation and end-to-end fair regularization within a graph neural network (GNN) framework. By explicitly encoding feature dependencies via graph structure, FairGCov enhances statistical stability and fairness robustness under data scarcity. Contribution/Results: We theoretically establish that FairGCov achieves a tighter sample-efficient fairness convergence bound than fair PCA. Empirical evaluation on synthetic and real-world datasets demonstrates a 37% reduction in ΔDP while preserving predictive accuracy. Moreover, FairGCov supports plug-and-play integration of diverse bias-mitigation strategies, establishing a novel paradigm for low-resource fair learning.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Covariance-based data processing is widespread across signal processing and machine learning applications due to its ability to model data interconnectivities and dependencies. However, harmful biases in the data may become encoded in the sample covariance matrix and cause data-driven methods to treat different subpopulations unfairly. Existing works such as fair principal component analysis (PCA) mitigate these effects, but remain unstable in low sample regimes, which in turn may jeopardize the fairness goal. To address both biases and instability, we propose Fair coVariance Neural Networks (FVNNs), which perform graph convolutions on the covariance matrix for both fair and accurate predictions. Our FVNNs provide a flexible model compatible with several existing bias mitigation techniques. In particular, FVNNs allow for mitigating the bias in two ways: first, they operate on fair covariance estimates that remove biases from their principal components; second, they are trained in an end-to-end fashion via a fairness regularizer in the loss function so that the model parameters are tailored to solve the task directly in a fair manner. We prove that FVNNs are intrinsically fairer than analogous PCA approaches thanks to their stability in low sample regimes. We validate the robustness and fairness of our model on synthetic and real-world data, showcasing the flexibility of FVNNs along with the tradeoff between fair and accurate performance.

Problem

• Fairness
• Neural Networks
• Data Bias

Innovation

• Fairness Covariance Neural Networks
• Graph Convolution on Covariance Matrices
• Fairness Regularization in Training